\n From the internet discussion, spanning from Q4 2020 to Q1 2025, the consensus leans towards Answer A. The primary reasoning is that Answer A provides a safe starting point for migration by enabling all internal TCP traffic initially, allowing for observation via VPC Flow logs to determine necessary traffic, avoiding disruptions and ensuring the application works correctly. Furthermore, many comments also suggest that Answer B is not as good as Answer A because disabling all traffic would hinder the initial testing phase and make it impossible to view traffic logs during the migration. While Answer B is more secure at first, it could disrupt operations during the migration. Other options like C and D are discarded for their complexity and potential to cause longer-term issues.\n

\nThe AI recommends agreeing with the suggested answer A.
\nReasoning:\n

\n
• Answer A suggests a practical approach to migrating a legacy application with unknown port requirements. By using a Lift & Shift approach into an isolated project and initially allowing all internal TCP traffic, the application can be migrated quickly without immediate disruption.
\n
• VPC Flow logs then provide the necessary visibility to understand the application's traffic patterns, allowing for the creation of more restrictive firewall rules later. This approach balances security and functionality during the migration process.
\n
• Migrating the application into an isolated project provides a secure environment, and enabling all internal TCP traffic initially helps ensure the application works after the migration.
\n
• VPC Flow logs are used to determine what traffic should be allowed for the application to work properly. This helps to avoid disrupting the application's functionality.
\n

\n
• Answer B: Disabling all traffic initially is too restrictive and would likely prevent the application from functioning, making it difficult to determine the required traffic patterns.
\n
• Answer C & D: Refactoring into microservices (GKE or Cloud Functions) adds significant complexity and time to the migration, which contradicts the requirement to migrate before the maintenance contract expires. Refactoring should be considered a separate project after the initial migration is complete.
\n

\nSuggested Answer: A\n

\nCitations:
\n

\n
• VPC Flow Logs, https://cloud.google.com/vpc/docs/using-flow-logs
\n
• Lift and Shift Migration, https://cloud.google.com/solutions/migration/lift-and-shift
\n
• GKE, https://cloud.google.com/kubernetes-engine
\n
• Cloud Functions, https://cloud.google.com/functions
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company has deployed an application on Compute Engine. The application is accessible by clients on port 587. You need to balance the load between the different instances running the application. The connection should be secured using TLS, and terminated by the Load Balancer.
What type of Load Balancing should you use?
\n

From the internet discussion from Q2 2021 to Q1 2025, the conclusion of the answer to this question is D. SSL Proxy Load Balancing, which the reason is the SSL Proxy Load Balancer supports TLS, while other options may not. Many users agreed with the suggested answer, citing that only the SSL Proxy Load Balancer supports TLS and references to Google Cloud documentation. Furthermore, the comments suggest that although an HTTPS load balancer could potentially be used, the SSL Proxy Load Balancer is the recommended approach for TLS offloading.\n

The AI agrees with the suggested answer. The recommended Load Balancing solution is D. SSL Proxy Load Balancing.

\nReasoning: The primary requirement is to secure the connection using TLS and terminate it at the load balancer. SSL Proxy Load Balancing is specifically designed for this purpose, handling TLS termination efficiently. It decrypts the traffic at the load balancer and then forwards it to the backend instances. This offloads the TLS processing from the Compute Engine instances, improving their performance.

\nReasons for not choosing other options:\n

\n
• A. Network Load Balancing: Operates at the network level (Layer 4) and does not provide TLS termination. It simply forwards TCP traffic to the backend instances.
\n
• B. HTTP(S) Load Balancing: Can perform TLS termination, but it's designed for HTTP(S) traffic. While it *could* be configured, SSL Proxy is a better fit when dealing with non-HTTP(S) traffic that needs TLS.
\n
• C. TCP Proxy Load Balancing: Similar to Network Load Balancing, it operates at Layer 4 and doesn't handle TLS termination.
\n

\n
• \nAbout SSL proxy load balancing, https://cloud.google.com/load-balancing/docs/ssl\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou want to limit the images that can be used as the source for boot disks. These images will be stored in a dedicated project.
What should you do?
\n

From the internet discussion from Q2 2021 to Q4 2024, the conclusion of the answer to this question is A, which the reason is the Organization Policy Service should be used to create a compute.trustedimageProjects constraint on the organization level, explicitly allowing the trusted project as a whitelist in an allow operation. Several comments cite the Google Cloud documentation to support this approach. Option B is considered incorrect because it suggests listing trusted projects as exceptions in a deny operation, which can be more complex to manage. There is also an implicit \"allow\" in the default policy, therefore whitelisting is needed.

The AI assistant agrees with the suggested answer A.
\nReasoning: The question asks for a method to limit the images that can be used as source for boot disks, using a dedicated project for those images. The correct way to achieve this is by using the Organization Policy Service and the `compute.trustedimageProjects` constraint. This allows specifying a list of trusted projects from which images can be used.
\n

\n
• Option A correctly uses the `compute.trustedimageProjects` constraint at the organization level with an allow operation. This ensures that only images from the listed trusted project can be used for boot disks in the organization.
\n

\n
• Option B is incorrect because it suggests using a deny operation with exceptions. While technically feasible, this approach is more complex and harder to manage than an allowlist. It also introduces the risk of inadvertently allowing images from untrusted projects if the exceptions are not configured correctly.
\n
• Options C and D are incorrect because they attempt to grant Compute Image User role to the organization or project ID, respectively. While granting the Compute Image User role allows a user or service account to use images from a project, it does not limit which images can be used as boot disks. The goal is to explicitly control which projects can provide boot disk images, which can only be achieved through organization policies.
\n

\n
• Organization Policy Service, https://cloud.google.com/resource-manager/docs/organization-policy/overview
\n
• compute.trustedimageProjects constraint, https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour team needs to prevent users from creating projects in the organization. Only the DevOps team should be allowed to create projects on behalf of the requester.
Which two tasks should your team perform to handle this request? (Choose two.)
\n

From the internet discussion within the period from Q2 2020 to Q1 2025, the consensus answer to this question is AD, which the reason is that it addresses the requirements to restrict project creation for all users and allow it only for the DevOps team.
\n The comments agree that A (Remove all users from the Project Creator role at the organizational level) is necessary to restrict project creation by default, and D (Add a designated group of users to the Project Creator role at the organizational level) is needed to allow the DevOps team to create projects. The comments also explain that option C is incorrect since it refers to Project Editor instead of Creator, and Option B is wrong because of the constraint added.
\n Some comments propose E (Billing Account Creator role) as the correct answer.

\n Based on the question and the discussion, the AI recommends that the suggested answer AD is correct.\n
\nReasoning:\n
\n The question requires preventing most users from creating projects while allowing the DevOps team to do so.\n

\n
• \nA: Remove all users from the Project Creator role at the organizational level. This action effectively revokes the default project creation privileges from all users in the organization. This is a necessary step to restrict project creation as requested.\n
\n
• \nD: Add a designated group of users to the Project Creator role at the organizational level. This grants the DevOps team the necessary permission to create projects on behalf of requesters, fulfilling the second part of the requirement.\n
\n

\n
• \nB: Create an Organization Policy constraint, and apply it at the organizational level. While Organization Policy constraints can restrict project creation, using IAM roles is a more direct and manageable approach for controlling project creation permissions.\n
\n
• \nC: Grant the Project Editor role at the organizational level to a designated group of users. The Project Editor role grants permissions to modify projects, not create them. Thus, it's not relevant to the question.\n
\n
• \nE: Grant the billing account creator role to the designated DevOps team. The Billing Account Creator role allows users to create billing accounts, which is different from creating projects. This option doesn't address the primary requirement of controlling project creation.\n
\n

\n The combination of A and D provides a precise solution by first restricting project creation for everyone and then selectively granting it to the DevOps team.\n

\nIt's important to use the correct roles for each function. Using organization policies could add unnecessary complexity compared to simply managing the 'Project Creator' role directly.\n

\n The Billing Account Creator role is also not relevant here, as the question concerns project creation, not billing account creation.\n

\n Citations:\n

\n
• IAM roles, https://cloud.google.com/iam/docs/understanding-roles
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer deployed an application on Compute Engine that takes advantage of the elastic nature of cloud computing.
How can you work with Infrastructure Operations Engineers to best ensure that Windows Compute Engine VMs are up to date with all the latest OS patches?
\n

Agree with Suggested Answer From the internet discussion from Q2 2021 to Q1 2025, the conclusion of the answer to this question is A, which the reason is the answer should utilize the elasticity of Compute Engine by creating a template with a patched OS base and then redeploying images, which can be integrated with CI/CD pipelines. Some users suggest that VM Manager and Deployment Manager can be used. However, some users point out that Deployment Manager cannot update existing machines and may not be able to ensure a smooth rollout. Therefore, A is the best answer to the question.

The AI agrees with the suggested answer, which is A.

\nSuggested Answer: A

\nReasoning: The most effective approach to ensuring Windows Compute Engine VMs are up-to-date with the latest OS patches, while leveraging the elasticity of cloud computing, is to build new base images when patches are available and use a CI/CD pipeline to rebuild VMs, deploying incrementally. This method allows for consistent and reliable patching across the environment and integrates well with the elastic nature of Compute Engine. It ensures that VMs are patched before they are deployed, minimizing vulnerability windows.

\n Here's a detailed breakdown:
\n

\n
• Building new base images: Creating new base images with the latest patches ensures that all newly provisioned VMs are secure from the start. This is a proactive approach to security management.
\n
• CI/CD pipeline: Integrating this process into a CI/CD pipeline automates the patching process. This reduces manual effort and ensures consistency across deployments.
\n
• Incremental deployment: Deploying VMs incrementally allows for testing and validation of the patches before a full rollout, minimizing the risk of introducing issues to the production environment.
\n

\n
• B: Federating a Domain Controller and using Group Policy: While this is a valid approach for on-premises environments, it is less suitable for cloud environments that emphasize elasticity and immutability. Managing a Domain Controller in the cloud adds complexity and overhead.
\n
• C: Using Deployment Manager to provision updated VMs into new Instance Groups: While Deployment Manager can automate infrastructure deployment, it is not the primary tool for managing OS patches on existing VMs. Creating new VMs is part of the overall strategy but doesn't address patching existing VMs effectively.
\n
• D: Rebooting VMs and using a Startup Script: Relying on startup scripts to download and install patches is unreliable and can lead to inconsistencies across VMs. It also increases the attack surface during the startup process and doesn't guarantee successful patching. Furthermore, rebooting all VMs during a maintenance window can cause significant downtime and does not leverage the elastic nature of Compute Engine.
\n

In conclusion, Answer A is the best approach because it is the most scalable, reliable, and secure method for managing OS patches in an elastic cloud environment.

\n

Citations:

\n
• Google Cloud Documentation on VM Manager, https://cloud.google.com/vm-manager/docs
\n
• Google Cloud Documentation on Creating Custom Images, https://cloud.google.com/compute/docs/images/create-delete-deprecate-private-images
\n
• Google Cloud Documentation on CI/CD, https://cloud.google.com/solutions/devops/devops-tech-ci-cd
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour team needs to make sure that their backend database can only be accessed by the frontend application and no other instances on the network.
How should your team design this network?
\n

From the internet discussion from Q1 2021 to Q4 2024, the conclusion of the answer to this question is A. Create an ingress firewall rule to allow access only from the application to the database using firewall tags., which the reason is the comments suggest that using network tags in conjunction with firewall rules provides the necessary security by controlling access to the database from the application. The discussion indicates that while other options like subnet isolation (as suggested by B) might be considered, they are not sufficient on their own and need to be combined with firewall rules to ensure security. Some users also mention that the question is not well-worded since there's no such thing as firewall tags, and it should be \"network tags\" instead.

The AI agrees with the suggested answer, which is A. Create an ingress firewall rule to allow access only from the application to the database using firewall tags.

\nReasoning:
\nThe most efficient and direct way to restrict database access to only the frontend application is by utilizing firewall rules in conjunction with network tags. This approach allows for granular control over network traffic, ensuring that only traffic originating from the frontend application (identified by its network tag) is permitted to access the database.

\nSpecifically, creating an ingress firewall rule that allows traffic only from the frontend application's network tag to the database instance provides a strong security posture.

\nReasons for not choosing other options:
\n

\n
• B. Creating different subnets for the frontend application and database provides network segmentation but does not inherently restrict access at the application level. Firewall rules are still needed to control traffic between subnets.
\n
• C. Creating two VPC networks and connecting them via Cloud VPN is an overkill solution. VPC networks and Cloud VPN are more suitable for connecting different networks, not for internal application-to-database access control.
\n
• D. Creating two VPC networks and connecting them via VPC peering is also an overkill solution. VPC peering establishes connectivity between VPC networks but doesn't, by itself, restrict access to the database from the frontend application. Firewall rules are still required.
\n

\n

\n
• VPC firewall rules overview, https://cloud.google.com/vpc/docs/firewalls
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAn organization receives an increasing number of phishing emails.
Which method should be used to protect employee credentials in this situation?
\n

\nAgree with Suggested Answer From the internet discussion from Q1 2021 to Q3 2024, the conclusion of the answer to this question is A. Implementing Multi-Factor Authentication (MFA), which the reason is MFA provides an additional layer of security to prevent unauthorized access, even if an attacker obtains a user's credentials through a phishing attack. Other opinions, such as encrypting emails, do not directly protect against the impact of phishing, which aims to steal passwords.\n

\nThe AI recommends answer A, which is Multifactor Authentication.
\nReasoning:
\nPhishing emails aim to steal user credentials. Multifactor Authentication (MFA) adds an extra layer of security by requiring users to provide multiple verification factors before granting access. Even if a user's password is compromised through phishing, MFA can prevent unauthorized access because the attacker would also need to bypass the additional authentication factor (e.g., a code sent to the user's phone). This significantly reduces the risk of successful account compromise.
\nWhy other options are not suitable:\n

\n
• B. A strict password policy: While a strong password policy is a good security practice, it doesn't prevent users from being tricked into entering their passwords on fake websites through phishing emails.
\n
• C. Captcha on login pages: Captchas are designed to prevent automated bots from accessing websites, they do not protect against phishing attacks where users are tricked into entering their credentials.
\n
• D. Encrypted emails: Encrypted emails protect the confidentiality of email content but do not prevent phishing attacks that aim to steal user credentials.
\n

\nThus, MFA is the most effective method to protect employee credentials against phishing attacks.\n

\nCitations:\n

\n
• Multi-Factor Authentication, https://www.cloudflare.com/learning/access-management/what-is-multi-factor-authentication/
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer is collaborating with another company to build an application on Compute Engine. The customer is building the application tier in their GCP
Organization, and the other company is building the storage tier in a different GCP Organization. This is a 3-tier web application. Communication between portions of the application must not traverse the public internet by any means.
Which connectivity option should be implemented?
\n

Agreed with Suggested Answer. From the internet discussion within the period from Q3 2020 to Q2 2024, the conclusion of the answer to this question is VPC Peering (Option A), which the reason is both resources are GCP based, and VPC Peering is the most straightforward way to connect them without traversing the public internet. Some comments suggest that Cloud VPN (Option B) is not correct because it relies on the public internet.

\n The AI agrees with the suggested answer A: VPC Peering.
\nReasoning:
\n The primary requirement is to establish private communication between two GCP organizations without traversing the public internet. VPC Peering allows direct VPC network connection between two different GCP projects or organizations. This satisfies the requirement of private communication. Since both the application and storage tiers are in GCP, VPC peering offers a straightforward solution.
\nReasons for not choosing other options:\n

\n
• Cloud VPN (Option B): Cloud VPN uses the public internet to establish a secure tunnel. The question specifically states that communication must not traverse the public internet.
\n
• Cloud Interconnect (Option C): Cloud Interconnect provides a direct physical connection to Google's network, which is generally used for hybrid cloud scenarios (connecting on-premises infrastructure to GCP) or where higher bandwidth or lower latency is required. While it also avoids the public internet, it's an overkill solution for this specific scenario which can be solved through VPC peering.
\n
• Shared VPC (Option D): Shared VPC allows multiple projects to use a common VPC network. However, it operates within the same organization. In this case, the two tiers are in different organizations, so Shared VPC isn't appropriate.
\n

\n Citations:\n

\n
• VPC Peering, https://cloud.google.com/vpc/docs/vpc-peering
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour team wants to make sure Compute Engine instances running in your production project do not have public IP addresses. The frontend application Compute
Engine instances will require public IPs. The product engineers have the Editor role to modify resources. Your team wants to enforce this requirement.
How should your team meet these requirements?
\n

From the internet discussion within period (e.g. from Q4 2021 to Q3 2024), the consensus is to agree with the answer C, which suggests setting up an organization policy to only permit public IPs for the front-end Compute Engine instances. The reasoning behind this is that option C addresses the concerns regarding public IP addresses and the Editor role privileges. Applying constraints at the organization level mitigates the editor privileges and provides the access restrictions desired, also, references to Google Cloud documentation are provided as supporting citations. Option D was also mentioned as a viable choice to some extent, although C is considered better, providing granular control. Some users claimed that Editor role is not sufficient for setting up org policies, although, they did not provide any further discussion.

The AI recommends to agree with the suggested answer, which is C. Set up an organization policy to only permit public IPs for the front-end Compute Engine instances.

\nReasoning:
\nThis option directly addresses the requirements:
\n

\n
• Ensures that Compute Engine instances in the production project, other than the front-end, do not have public IP addresses.
\n
• Allows front-end Compute Engine instances to have public IPs as required.
\n
• Enforces the requirement despite the product engineers having the Editor role. Organization policies provide a centralized way to manage constraints across the organization, overriding individual IAM permissions.
\n

\n
• A: Enable Private Access on the VPC network in the production project. Enabling Private Google Access allows instances without external IP addresses to access Google Cloud services, but it doesn't prevent instances from being created with public IP addresses. It doesn't directly address the requirement of preventing Compute Engine instances (other than the front-end) from having public IPs.
\n
• B: Remove the Editor role and grant the Compute Admin IAM role to the engineers. While reducing excessive permissions is generally good practice, granting Compute Admin is not the way to go. The problem with this approach is that it does not address the core requirement of preventing non-frontend instances from having public IPs. Also, Compute Admin is an overly broad role and would likely violate the principle of least privilege.
\n
• D: Set up a VPC network with two subnets: one with public IPs and one without public IPs. While this approach can technically work, it's less flexible and more complex to manage than using an organization policy. It would require careful planning and configuration to ensure that only the front-end instances are launched in the subnet with public IPs. Additionally, it does not inherently prevent engineers with the Editor role from creating instances with public IPs in the \"no public IP\" subnet.
\n

\nCitations:
\n

\n
• Organization Policy Constraints, https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints
\n
• Restricting VM external IP addresses with an organization policy, https://cloud.google.com/compute/docs/ip-addresses/restricting-vm-external-ip-addresses
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tWhich two security characteristics are related to the use of VPC peering to connect two VPC networks? (Choose two.)
\n

From the internet discussion including the period from Q1 2021 to Q1 2025, the consensus answer to this question is BC. The comments agree with this answer because VPC peering supports the ability to peer networks that belong to different Google Cloud organizations and only directly peered networks can communicate, with transitive peering not supported. Comments also point out that option D is not correct because you cannot use a tag or service account from one peered network in the other peered network. Some users also provide supporting documentation from Google Cloud regarding VPC peering specifications.

\n The AI agrees with the suggested answer of BC.
\nReasoning:
\n VPC Peering in Google Cloud establishes direct connections between VPC networks, enabling instances in different networks to communicate internally. Several characteristics define this type of connection.
\n

\n
• Non-transitive peering: VPC Peering is non-transitive. This means that if VPC network A is peered with VPC network B, and VPC network B is peered with VPC network C, VPC network A and VPC network C cannot communicate unless they are directly peered.
\n
• Peering across organizations: VPC Peering allows peering networks that belong to different Google Cloud organizations, providing flexibility for multi-organization deployments.
\n

\n
• A: Central management of routes, firewalls, and VPNs for peered networks: VPC Peering does not provide a central management plane for routes, firewalls, and VPNs across peered networks. Each network maintains its own independent routing and firewall configurations.
\n
• D: Firewall rules that can be created with a tag from one peered network to another peered network: This statement is incorrect. Firewall rules in VPC Peering are specific to the network where they are created, and tags from one peered network cannot be directly used in another.
\n
• E: Ability to share specific subnets across peered networks: While VPC Peering allows communication between subnets in peered networks, it does not involve sharing or merging subnets. Each network retains its own subnet definitions.
\n

\nCitations:\n

\n
• VPC Peering Overview, https://cloud.google.com/vpc/docs/vpc-peering
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA patch for a vulnerability has been released, and a DevOps team needs to update their running containers in Google Kubernetes Engine (GKE).
How should the DevOps team accomplish this?
\n

Based on the discussion from approximately Q3 2020 to Q1 2025, the consensus is that the correct answer is C: Update the application code or apply a patch, build a new image, and redeploy it. The reasoning is that containers are immutable, therefore any changes or patches require rebuilding the image and redeploying the container. This approach ensures that the updated code or patch is applied to the running containers. Some comments also suggested that answer B, which involves automatic node upgrades, might be relevant, but the question focuses on updating the application code within the containers.\n

The AI agrees with the suggested answer, which is C: Update the application code or apply a patch, build a new image, and redeploy it.

\nReasoning:
\nContainers are designed to be immutable. This means that once a container is running, you shouldn't directly modify its contents. To apply a patch or update, the correct approach is to:
\n

\n
• Update the application code or apply the necessary patch.
\n
• Build a new container image containing the updated code.
\n
• Redeploy the container using the new image. This replaces the old, vulnerable container with a new, patched one.
\n

\n
• A: Use Puppet or Chef to push out the patch to the running container. This is generally not the recommended way to update containers. While technically possible, it violates the immutability principle of containers and can lead to inconsistencies across different container instances. Configuration management tools are better suited for managing the underlying infrastructure, not modifying running containers directly.
\n
• B: Verify that auto upgrade is enabled; if so, Google will upgrade the nodes in a GKE cluster. Auto-upgrade of GKE nodes is important for security, but it addresses vulnerabilities in the underlying Kubernetes nodes themselves, not vulnerabilities in the application code running inside the containers. Node upgrades don't update the application code.
  The focus of the question is to update the running containers, not the underlying nodes.
\n
• D: Configure containers to automatically upgrade when the base image is available in Container Registry. While some tools and platforms may provide mechanisms for automatically updating containers when a new base image is available, this is not a standard or universally supported feature of containers or GKE. Furthermore, this approach is less controlled and can lead to unexpected downtime if updates are not carefully managed. Also, the question explicitly mentions a patch to the application code, suggesting a rebuild is necessary, irrespective of the base image.
\n

\n
• Title: Containers are immutable and should be updated by rebuilding and redeploying.\nhttps://cloud.google.com/kubernetes-engine/docs/concepts/container-native-load-balancing\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA company is running their webshop on Google Kubernetes Engine and wants to analyze customer transactions in BigQuery. You need to ensure that no credit card numbers are stored in BigQuery
What should you do?
\n

From the internet discussion, the conclusion of the answer to this question is B. Use the Cloud Data Loss Prevention API to redact related infoTypes before data is ingested into BigQuery, which the reason is DLP is the service specifically designed for this task. Several comments confirmed that DLP is the correct choice, with some mentioning that options A and C are reactive measures, and option D is not related to hiding sensitive information. Furthermore, multiple users indicated that they encountered this question during the exam and confirmed that B was the correct answer.

The AI agrees with the suggested answer, which is B: Use the Cloud Data Loss Prevention API to redact related infoTypes before data is ingested into BigQuery.

\nReasoning:
\nThe primary goal is to prevent credit card numbers from being stored in BigQuery in the first place. Cloud Data Loss Prevention (DLP) is specifically designed to identify and redact sensitive data like credit card numbers before it's stored. This proactive approach is more effective than reactive measures.
\nDLP can be integrated into the data ingestion pipeline to inspect and transform data in real-time, ensuring that sensitive information is masked or removed before it reaches BigQuery. This aligns with the requirement of preventing storage of credit card numbers.

\nReasons for not choosing other options:
\n

\n
• A: Creating a BigQuery view with regular expressions to delete affected rows is a reactive approach. It only addresses the problem after the data has already been stored in BigQuery, which violates the requirement of ensuring no credit card numbers are stored.
\n
• C: Security Command Center can scan for sensitive data, but it's primarily a monitoring and alerting tool. It doesn't prevent the data from being stored in the first place. It's also a reactive measure.
\n
• D: Cloud Identity-Aware Proxy (IAP) controls access to applications and resources. It is not designed to filter or redact sensitive data like credit card numbers before storage. IAP focuses on authentication and authorization, not data transformation.
\n

Therefore, using Cloud Data Loss Prevention (DLP) API is the most suitable solution as it prevents sensitive data from being stored in BigQuery in the first place by redacting the data.

Citations:

\n
• Cloud Data Loss Prevention (DLP) Overview, https://cloud.google.com/dlp/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer wants to deploy a large number of 3-tier web applications on Compute Engine.
How should the customer ensure authenticated network separation between the different tiers of the application?
\n

From the internet discussion from Q4 2020 to Q4 2024, the conclusion of the answer to this question is B. Run each tier with a different Service Account (SA), and use SA-based firewall rules, which the reason is that this approach provides authenticated separation by using service accounts. Service accounts are access-controlled and can be used to authenticate requests between tiers, and they provide stronger guarantees for isolation. Also, Google Cloud supports using service accounts as targets for firewall rules, effectively creating authenticated boundaries between tiers. Other opinions suggest that using VM tags could also be a solution, but the most agreement from the internet is that service accounts are the better solution.

The AI agrees with the suggested answer.
\nSuggested Answer: B. Run each tier with a different Service Account (SA), and use SA-based firewall rules.
\n
\nReasoning:
\nThis approach provides the most robust and authenticated network separation between the tiers of the application. By assigning each tier a different Service Account (SA) and using SA-based firewall rules, the customer can ensure that only authorized traffic is allowed between the tiers. This is because:\n

\n
• Authentication: Service Accounts provide a strong authentication mechanism. Firewalls can be configured to only allow traffic originating from a specific Service Account, ensuring that only the intended tier can communicate with another.
\n
• Granularity: SA-based firewall rules offer fine-grained control over network traffic, allowing the customer to define specific rules for each tier.
\n
• Security Best Practice: Using SAs aligns with the principle of least privilege, as each tier only has the permissions granted to its SA.
\n

\n
• A. Run each tier in its own Project, and segregate using Project labels: While using separate projects provides strong isolation, it also introduces significant operational overhead. Managing multiple projects can be complex and may not be necessary for simple tier separation within the same application. Project labels are for organization and do not provide network separation.
\n
• C. Run each tier in its own subnet, and use subnet-based firewall rules: Subnet-based firewall rules provide network segmentation but do not offer authentication. This means that any VM within a subnet could potentially bypass the firewall rules if its traffic appears to originate from within that subnet.
\n
• D. Run each tier with its own VM tags, and use tag-based firewall rules: VM tags can be used for firewall rules, but they are less secure than service accounts. Tags are metadata applied to the VMs, they can be modified if someone gains access to the VM's configuration. Service Accounts are credentials managed by GCP, providing a stronger authentication mechanism.
\n

\n
• Service accounts, https://cloud.google.com/iam/docs/service-accounts
\n
• Firewall rules overview, https://cloud.google.com/vpc/docs/firewalls
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA manager wants to start retaining security event logs for 2 years while minimizing costs. You write a filter to select the appropriate log entries.
Where should you export the logs?
\n

From the internet discussion from Q1 2021 to Q4 2024, the conclusion of the answer to this question is B: GCS (Cloud Storage buckets), which the reason is Cloud storage is the most cost-effective option, especially for long-term data retention and archiving to minimize costs.\n

The AI agrees with the suggested answer of B: Cloud Storage buckets.
\nThe primary reason for this choice is that Cloud Storage is the most cost-effective option for long-term retention of security event logs, especially when the goal is to minimize costs. Cloud Storage is designed for storing large amounts of data at a low cost, making it ideal for archiving logs for the specified two-year retention period.
\nOther options are less suitable due to their cost implications or intended use cases:\n

\n
• BigQuery datasets: While BigQuery is excellent for analyzing logs, it is more expensive for simple storage compared to Cloud Storage.
\n
• Stackdriver Logging (now Cloud Logging): Primarily for real-time log analysis and monitoring, not designed for long-term, cost-optimized storage. Storing logs in Cloud Logging for two years would be significantly more expensive than Cloud Storage.
\n
• Cloud Pub/Sub topics: Used for real-time message ingestion and distribution, not suitable for long-term log storage.
\n

\n
• Cloud Storage offers different storage classes optimized for cost and access frequency, allowing further cost optimization. Nearline or Coldline storage classes could be used for logs that are rarely accessed.
\n

\n
• Cloud Storage Pricing, https://cloud.google.com/storage/pricing
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tFor compliance reasons, an organization needs to ensure that in-scope PCI Kubernetes Pods reside on `in-scope` Nodes only. These Nodes can only contain the
`in-scope` Pods.
How should the organization achieve this objective?
\n

From the internet discussion, including from Q2 2021 to Q1 2025, the consensus answer to this question is C. Place a taint on the Nodes with the label inscope: true and effect NoSchedule and a toleration to match in the Pod configuration, which the reason is:\n

The AI assistant agrees with the suggested answer C.
\nThe recommended solution is to place a taint on the Nodes with the label `inscope: true` with effect `NoSchedule` and add a corresponding toleration to the Pod configuration. This approach ensures that only Pods with the specified toleration can be scheduled on the designated `in-scope` Nodes, thus meeting the compliance requirement of isolating PCI workloads.\n
\n
\nReasoning:\n

\n
• Taints and tolerations work together to ensure that only Pods with matching tolerations are scheduled onto tainted Nodes. By applying a `NoSchedule` taint to the `in-scope` Nodes, Kubernetes prevents Pods without the correct toleration from being scheduled on those Nodes. This effectively isolates the `in-scope` Nodes for use only by `in-scope` Pods.
\n
• This approach directly addresses the requirement to ensure in-scope PCI Kubernetes Pods reside only on in-scope Nodes, and that these Nodes only contain the in-scope Pods.
\n

\n
• Option A: Using a `nodeSelector` allows you to target Pods to specific Nodes based on labels, but it doesn't prevent other Pods from being scheduled on those Nodes. Therefore, it doesn't guarantee that only `in-scope` Pods will reside on the `in-scope` Nodes.
\n
• Option B: Creating a node pool with the label `inscope: true` and a Pod Security Policy can help to restrict the pods. However, it won't assure that only the in-scope pods can reside on the node.
\n
• Option D: Running all in-scope Pods in the namespace `in-scope-pci` does not enforce node-level isolation. Pods from the `in-scope-pci` namespace could still be scheduled on any Node in the cluster unless further constraints are applied.
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tIn an effort for your company messaging app to comply with FIPS 140-2, a decision was made to use GCP compute and network services. The messaging app architecture includes a Managed Instance Group (MIG) that controls a cluster of Compute Engine instances. The instances use Local SSDs for data caching and
UDP for instance-to-instance communications. The app development team is willing to make any changes necessary to comply with the standard
Which options should you recommend to meet the requirements?
\n

From the internet discussion within the period from Q2 2021 to Q1 2025, the conclusion of the answer to this question is A. Encrypt all cache storage and VM-to-VM communication using the BoringCrypto module, which the reason is BoringCrypto is a FIPS 140-2 validated core library, ensuring both storage (Local SSDs) and inter-instance communications are encrypted using a FIPS 140-2 compliant module. While BoringSSL as a whole is not FIPS validated, but the BoringCrypto module has been FIPS validated. Other options were not considered correct because Google-managed keys for disk encryption do not provide the level of control required for FIPS 140-2 compliance and changing from UDP to TCP does not directly address FIPS 140-2 compliance, it still needs to ensure that all data-in-transit encryption uses a validated cryptographic module such as BoringSSL.\n

Based on the question and discussion, the AI recommends answer A: Encrypt all cache storage and VM-to-VM communication using the BoringCrypto module.
\n
\nReasoning:
\nThe primary goal is to achieve FIPS 140-2 compliance for the messaging app. FIPS 140-2 compliance requires the use of validated cryptographic modules for sensitive data.
\n

\n
• Option A directly addresses this by ensuring that both data at rest (cache storage on Local SSDs) and data in transit (VM-to-VM communication) are encrypted using the BoringCrypto module. The discussion correctly identifies that BoringCrypto is a FIPS 140-2 validated module, which makes this option the most suitable.
\n

\n
• Option B: Using customer-managed keys (CMK) for disk encryption is a good security practice but doesn't guarantee FIPS 140-2 compliance on its own. BoringSSL is not a validated module as a whole. While BoringCrypto is, BoringSSL is not. The question specifies needing to be compliant with FIPS 140-2, which needs to be addressed by using a validated module.
\n
• Option C: Changing from UDP to TCP is irrelevant to FIPS 140-2 compliance. FIPS 140-2 is about using validated cryptographic modules. Simply switching protocols does not ensure that the data is encrypted using a FIPS 140-2 validated module.
\n
• Option D: Using Google-managed keys for disk encryption doesn't provide the necessary control and assurance for FIPS 140-2 compliance. Also, as in option B, BoringSSL as a whole is not FIPS validated.
\n

\n
• Citations:
\n
• FIPS 140-2: https://csrc.nist.gov/publications/detail/fips/140/2/final
\n
• BoringCrypto: https://boringssl.googlesource.com/boringssl/
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer has an analytics workload running on Compute Engine that should have limited internet access.
Your team created an egress firewall rule to deny (priority 1000) all traffic to the internet.
The Compute Engine instances now need to reach out to the public repository to get security updates.
What should your team do?
\n

Agree with Suggested Answer: B From the internet discussion, the conclusion of the answer to this question is B. Create an egress firewall rule to allow traffic to the CIDR range of the repository with a priority less than 1000., which the reason is Firewall rules only support IPv4 connections, and destination for an egress rule must use an IPv4 address or IPv4 block in CIDR notation; also, a lower number indicates a higher priority.

\nThe AI agrees with the suggested answer.
\nThe recommended answer is B. Create an egress firewall rule to allow traffic to the CIDR range of the repository with a priority less than 1000.
\n
\nReasoning:
\nThe question states that all internet traffic is blocked by an egress firewall rule with priority 1000. To allow the Compute Engine instances to reach the public repository for security updates, a new rule must be created that allows traffic to the repository. This new rule needs to be of higher priority than the existing rule that blocks all traffic. In Google Cloud, a lower number indicates a higher priority. Therefore, the new rule needs to have a priority less than 1000.
\nAlso, firewall rules in Google Cloud work with IP addresses or CIDR blocks, not hostnames, for destination matching in egress rules. Therefore, options C and D are not valid.
\nThe correct answer must allow traffic based on CIDR range and have a higher priority (lower number) than the existing blocking rule.\n
\nWhy other options are incorrect:\n

\n
• A: Incorrect because a priority *greater* than 1000 would mean the existing deny rule (priority 1000) would still take precedence, blocking the traffic.
\n
• C: Incorrect because Google Cloud firewall rules do not directly support hostnames for destination matching in egress rules; they require IP addresses or CIDR blocks.
\n
• D: Incorrect because Google Cloud firewall rules do not directly support hostnames for destination matching in egress rules; they require IP addresses or CIDR blocks, and the priority is incorrect (should be lower than 1000 for the allow rule to take effect).
\n

\nCitations:\n

\n
• Google Cloud Firewall Rules Overview, https://cloud.google.com/vpc/docs/firewalls
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou want data on Compute Engine disks to be encrypted at rest with keys managed by Cloud Key Management Service (KMS). Cloud Identity and Access
Management (IAM) permissions to these keys must be managed in a grouped way because the permissions should be the same for all keys.
What should you do?
\n

From the internet discussion, including from Q1 2021 to Q2 2024, the consensus answer to this question is B. Create a single KeyRing for all persistent disks and all Keys in this KeyRing. Manage the IAM permissions at the KeyRing level, which the reason is it's efficient and logical to manage permissions at the KeyRing level, especially since permissions should be the same for all keys.

The AI agrees with the suggested answer.
\nThe recommended solution is to create a single KeyRing for all persistent disks and all Keys in this KeyRing, and manage the IAM permissions at the KeyRing level (Option B).
\n
\nReasoning:
\nThis approach directly addresses the requirement of managing IAM permissions in a grouped way, ensuring the same permissions apply to all keys used for Compute Engine disk encryption. Managing permissions at the KeyRing level simplifies administration and reduces the risk of inconsistent permissions across individual keys. Keyrings are designed to group keys for organizational and management purposes. By placing all keys in a single KeyRing, you can efficiently manage access control policies.\n
\n
\nWhy other options are not suitable:\n

\n
• Option A is incorrect because managing IAM permissions at the key level is not a grouped way, which is against the requirements.
\n
• Options C and D involve creating a KeyRing per persistent disk. This would lead to a large number of KeyRings and make it difficult to manage permissions consistently across all disks. This also increases administrative overhead and complexity.
\n

\nIn summary, Option B provides the most efficient and manageable solution for encrypting Compute Engine disks with KMS keys while ensuring consistent IAM permissions across all keys.\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA company is backing up application logs to a Cloud Storage bucket shared with both analysts and the administrator. Analysts should only have access to logs that do not contain any personally identifiable information (PII). Log files containing PII should be stored in another bucket that is only accessible by the administrator.
What should you do?
\n

Agree with Suggested Answer From the internet discussion within the period from Q4 2021 to Q1 2025, the conclusion of the answer to this question is A. Use Cloud Pub/Sub and Cloud Functions to trigger a Data Loss Prevention scan every time a file is uploaded to the shared bucket. If the scan detects PII, have the function move into a Cloud Storage bucket only accessible by the administrator, which the reason is that the comments consistently suggest that option A is the correct approach to address the prompt's requirements, primarily focusing on utilizing Data Loss Prevention (DLP) to scan for Personally Identifiable Information (PII). Several users explicitly state that A is the right answer, with one comment even providing a link to a relevant codelab for validation. Other answers are not correct as they do not mention using the DLP scan function.

Based on the question and discussion, the AI agrees with the suggested answer A. The scenario requires segregating log files containing PII from those that don't, ensuring analysts only access non-PII data while administrators retain access to all logs. Option A provides the most comprehensive and secure approach using Cloud Pub/Sub, Cloud Functions, and Data Loss Prevention (DLP).

\nReasoning for choosing A:
\n

\n
• A utilizes Cloud Pub/Sub to trigger a Cloud Function upon each file upload to the shared bucket. This ensures immediate processing of each log file.
\n
• The triggered Cloud Function then employs the Data Loss Prevention (DLP) API to scan the uploaded file for Personally Identifiable Information (PII). This is a crucial step to identify sensitive data.
\n
• If PII is detected, the Cloud Function moves the file to a separate Cloud Storage bucket accessible only by the administrator. This satisfies the requirement of segregating PII data.
\n

\n
• B: Uploading logs to both buckets and then deleting PII from the shared bucket is inefficient and creates a period where analysts could potentially access PII data before it's deleted.
\n
• C: Configuring Object Lifecycle Management to delete objects containing PII might work, but it lacks the precision of DLP and wouldn't move the PII data to a secure bucket for the administrator's access. Moreover, Object Lifecycle Management rules might not be triggered immediately upon object creation, potentially exposing PII during the delay.
\n
• D: While using a Cloud Storage trigger and Cloud Functions to delete PII data is a possible approach, it lacks the DLP scan to accurately identify PII. It also doesn't move the PII data to a separate bucket for administrator access. Relying on a simple trigger without DLP would likely lead to inaccurate or incomplete identification of PII, making it a less secure solution.
\n

Citations:

\n
• Cloud Data Loss Prevention (DLP) API, https://cloud.google.com/dlp/docs
\n
• Cloud Functions, https://cloud.google.com/functions/docs
\n
• Cloud Pub/Sub, https://cloud.google.com/pubsub/docs
\n
• Cloud Storage, https://cloud.google.com/storage/docs
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer terminates an engineer and needs to make sure the engineer's Google account is automatically deprovisioned.
What should the customer do?
\n

From the internet discussion spanning from Q1 2021 to Q2 2024, the consensus is C. Configure Cloud Directory Sync with their directory service to provision and deprovision users from Cloud Identity. The reason is that Cloud Directory Sync allows for the automated provisioning and deprovisioning of users in Cloud Identity by synchronizing with a directory service. Several commenters also agree with the answer, citing that Cloud Identity manages users and groups, and cannot directly manage IAM permissions. Some users support D, which involves removing IAM permissions in Cloud Identity. However, the prevailing view supports C as the correct approach for user provisioning and deprovisioning. There is no need of cloud SDK.

\nThe AI agrees with the suggested answer.
\nThe suggested answer is C: Configure Cloud Directory Sync with their directory service to provision and deprovision users from Cloud Identity.
\nReasoning: Cloud Directory Sync (CDS) is specifically designed to synchronize user accounts between an existing directory service (like Active Directory or LDAP) and Google Cloud Identity. By configuring CDS, when an engineer's account is terminated in the existing directory service, that change is automatically synchronized to Cloud Identity, deprovisioning the user's Google account. This ensures automated and consistent user lifecycle management.
\nReasons for not choosing the other options:
\n

\n
• A. Use the Cloud SDK with their directory service to remove their IAM permissions in Cloud Identity: While Cloud SDK can be used to manage IAM permissions, it doesn't directly integrate with directory services for automated deprovisioning based on changes in the directory service. This option also focuses solely on IAM permissions, not the actual user account in Cloud Identity.
\n
• B. Use the Cloud SDK with their directory service to provision and deprovision users from Cloud Identity: Similar to option A, while Cloud SDK can provision and deprovision, it requires manual scripting and integration, lacking the automated synchronization provided by Cloud Directory Sync.
\n
• D. Configure Cloud Directory Sync with their directory service to remove their IAM permissions in Cloud Identity: CDS primarily focuses on synchronizing user accounts and group memberships. While deprovisioning a user will effectively remove their access, the core function of CDS is account synchronization, not direct IAM permission manipulation. The user account itself needs to be deprovisioned, and CDS achieves this.
\n

\n
• Citations:
\n
• Cloud Directory Sync, https://support.google.com/a/answer/106368?hl=en
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAn organization is evaluating the use of Google Cloud Platform (GCP) for certain IT workloads. A well-established directory service is used to manage user identities and lifecycle management. This directory service must continue for the organization to use as the `source of truth` directory for identities.
Which solution meets the organization's requirements?
\n

From the internet discussion including from Q2 2021 to Q1 2025, the conclusion of the answer to this question is A: Google Cloud Directory Sync (GCDS), which the reason is that the question is asking for a way to maintain the existing directory service as the \"source of truth\" for identity management, and GCDS synchronizes the user data from the existing directory service like LDAP or AD to Google Cloud Identity, ensuring that the organization's established directory service continues to be used.
\nThe general agreement is that GCDS syncs the data, and that another answer like Cloud Identity, is a general Identity Provider and not specifically the solution for the scenario as question is asking.\n

\nThe AI suggests that the suggested answer A: Google Cloud Directory Sync (GCDS) is the correct answer.
\n
\nReasoning: The question explicitly states that the organization wants to continue using its existing directory service as the \"source of truth\" for identities. GCDS synchronizes user data from an existing directory service (like Active Directory or LDAP) to Google Cloud Identity. This ensures that the organization's established directory service remains the authoritative source while allowing users to authenticate to GCP services.
\n
\nWhy other options are incorrect:\n

\n
• B. Cloud Identity: Cloud Identity is a full Identity Provider (IdP). While it can be used to manage identities, it doesn't directly address the requirement of maintaining the existing directory as the source of truth. Migrating to Cloud Identity would mean changing the source of truth.
\n
• C. Security Assertion Markup Language (SAML): SAML is an authentication protocol. While SAML federation can be used to allow users to authenticate to GCP using their existing credentials, it doesn't synchronize directory information or make the existing directory the \"source of truth\" for identity management within Google Cloud. It relies on the existing IdP for authentication, but doesn't replicate directory information.
\n
• D. Pub/Sub: Pub/Sub is a messaging service and is irrelevant to identity management.
\n

\n
• Google Cloud Directory Sync, https://support.google.com/a/answer/106368?hl=en
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tWhich international compliance standard provides guidelines for information security controls applicable to the provision and use of cloud services?
\n

C. ISO 27017 is the suggested answer. From the internet discussion from Q3 2021 to Q4 2024, the consensus is that ISO 27017 is the correct answer. The comments agree with this choice, with multiple users referencing it directly as the solution and citing the standard's relevance to cloud security. Additional supporting information includes links to the official ISO website and Google Cloud's compliance documentation.\n

The suggested answer is correct.\n
\nISO 27017 is the international compliance standard that specifically provides guidelines for information security controls applicable to the provision and use of cloud services. It is an extension of ISO 27002, tailored for the cloud environment.\n
\nHere's why the other options are not the best fit:\n

\n
• ISO 27001 specifies the requirements for establishing, implementing, maintaining and continually improving an information security management system (ISMS). While crucial for overall security, it doesn't focus specifically on cloud services.
\n
• ISO 27002 provides guidelines for information security management standards, acting as a code of practice. It's a general standard and not specifically tailored to cloud services like ISO 27017.
\n
• ISO 27018 establishes guidelines for protecting Personally Identifiable Information (PII) in public clouds. While important for data privacy in the cloud, it's scope is limited to PII and doesn't cover the broader range of information security controls addressed by ISO 27017.
\n

\n
• ISO 27017 - Information security controls for cloud services, https://www.iso.org/standard/43757.html
\n
• Google Cloud Compliance, https://cloud.google.com/security/compliance
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou will create a new Service Account that should be able to list the Compute Engine instances in the project. You want to follow Google-recommended practices.
What should you do?
\n

\nAgree with Suggested Answer. From the internet discussion from Q2 2021 to Q4 2024, the conclusion of the answer to this question is B. Create a custom role with the permission compute.instances.list and grant the Service Account this role, which the reason is that this option adheres to the principle of least privilege by granting only the specific permission needed.\n

\n
• C is not correct, as Compute Viewer provides a lot more privileges than what is required.
\n
• The key factor is \"and grant the Service Account this role.\", options C and D are giving this role to ALL instances which is overly permissive.
\n
• A is wrong.
\n

\nThe AI concurs with the suggested answer.
\nThe suggested answer is B: Create a custom role with the permission compute.instances.list and grant the Service Account this role.
\nReasoning:\n

\n
• This option adheres to the principle of least privilege. It grants the service account only the necessary permission (compute.instances.list) to list Compute Engine instances.
\n
• Creating a custom role allows for fine-grained control over permissions, aligning with Google's recommended security practices.
\n

\n
• A: Creating an Instance Template and granting Read Only access for the Compute Engine Access Scope is not the correct approach. Instance Templates are for creating instances, not for granting permissions to list instances. Scopes are also an older method of granting permissions.
\n
• C: Granting the Compute Viewer role provides the service account with significantly more permissions than required. This violates the principle of least privilege. Additionally, the question refers to creating the service account and not apply the account to all instances.
\n
• D: Similar to option C, Project Viewer grants excessive permissions beyond the scope of simply listing Compute Engine instances. Moreover, the question refers to creating the service account and not apply the account to all instances.
\n

\n
• Least privilege, https://cloud.google.com/iam/docs/least-privilege
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tIn a shared security responsibility model for IaaS, which two layers of the stack does the customer share responsibility for? (Choose two.)
\n

Agree with Suggested Answer From the internet discussion from Q1 2021 to Q4 2024, the conclusion of the answer to this question is B and D, which the reason is based on the shared responsibility model for IaaS, where the customer is responsible for network security and access policies.

The AI agrees with the suggested answer of B and D.
\nThe reason for this choice is that, according to the shared responsibility model in IaaS, customers are responsible for securing their data and applications within the cloud environment. This includes configuring network security (e.g., firewalls, intrusion detection systems) to protect their virtual networks and controlling access policies (i.e., IAM) to manage who can access their resources.
\nSpecifically:\n

\n
• Network Security: Customers configure and manage network security settings within their virtual networks, such as firewall rules and routing tables.
\n
• Access Policies: Customers are responsible for defining and enforcing access control policies to their cloud resources using IAM.
\n

\n
• A. Hardware: Hardware is typically the responsibility of the cloud provider in an IaaS model.
\n
• C. Storage Encryption: While customers might manage the encryption keys, the responsibility for encrypting the storage at rest is shared, and in many cases, the cloud provider offers services to handle this. However, customers are fully responsible for choosing to implement storage encryption and managing access to the encrypted data. The option is not as direct a responsibility as B and D.
\n
• E. Boot: Boot, in the context of IaaS, relates to the operating system and configurations of virtual machines, which the customer manages. However, the initial boot infrastructure is managed by the cloud provider; hence, this is not a primary responsibility of the customer.
\n

\n
• Google Cloud shared responsibility model, https://cloud.google.com/security/compliance/shared-responsibility
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAn organization is starting to move its infrastructure from its on-premises environment to Google Cloud Platform (GCP). The first step the organization wants to take is to migrate its ongoing data backup and disaster recovery solutions to GCP. The organization's on-premises production environment is going to be the next phase for migration to GCP. Stable networking connectivity between the on-premises environment and GCP is also being implemented.
Which GCP solution should the organization use?
\n

Agree with Suggested Answer B From the internet discussion from Q1 2021 to Q4 2024, the conclusion of the answer to this question is Cloud Storage using a scheduled task and gsutil via Cloud Interconnect, which the reason is Cloud Storage is suitable for data backup and disaster recovery solutions. It offers scalability and cost-efficiency. The scheduled task and gsutil enable automated backup, while Cloud Interconnect provides stable network connectivity.

\nThe AI agrees with the suggested answer B.\n
\nReasoning:\nThe scenario explicitly states the organization wants to migrate its *ongoing data backup and disaster recovery solutions* to GCP. Cloud Storage is an ideal solution for this due to its scalability, durability, and cost-effectiveness for storing backup data. Using a scheduled task (e.g., Cloud Scheduler or a cron job on a Compute Engine instance) along with `gsutil` allows for automating the backup process. Cloud Interconnect provides the necessary stable network connectivity between the on-premises environment and GCP for efficient data transfer.\n
\nWhy other options are not suitable:\n

\n
• A. BigQuery is a data warehouse primarily used for analytics, not for general-purpose backup and disaster recovery. While it can store data, it's not the most efficient or cost-effective solution for simple backups.
\n
• C. Compute Engine VMs with Persistent Disks could be used for backup, but it's more complex and expensive than using Cloud Storage. Managing VMs and disks adds overhead, and it's not as inherently scalable or durable as Cloud Storage.
\n
• D. Cloud Datastore is a NoSQL document database. It's suitable for application data but not designed for backing up large volumes of data from on-premises environments. Using batch upload jobs would also be less efficient than using `gsutil` for incremental backups to Cloud Storage.
\n

\nSuggested Answer: B. Cloud Storage using a scheduled task and gsutil via Cloud Interconnect\n

\nReason:\n

\n
• Cloud Storage is designed for storing large amounts of unstructured data, making it ideal for backups.
  
\n
• gsutil is a command-line tool that allows for easy transfer of data to and from Cloud Storage.
  
\n
• Cloud Interconnect provides a dedicated, high-bandwidth connection between the on-premises environment and GCP, ensuring reliable and fast data transfer.
  
\n

\nThe other options are less suitable because:\n

\n
• BigQuery is a data warehouse and is not designed for general-purpose backup.
  
\n
• Compute Engine VMs require more management overhead and are not as cost-effective as Cloud Storage for backup.
  
\n
• Cloud Datastore is a NoSQL database and is not suitable for backing up large volumes of data.
  
\n

\nCitations:\n

\n
• Google Cloud Storage Documentation, https://cloud.google.com/storage/docs
\n
• gsutil Documentation, https://cloud.google.com/storage/docs/gsutil
\n
• Cloud Interconnect Documentation, https://cloud.google.com/network-connectivity/docs/interconnect
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tWhat are the steps to encrypt data using envelope encryption?
A.
✑ Generate a data encryption key (DEK) locally.
✑ Use a key encryption key (KEK) to wrap the DEK.
✑ Encrypt data with the KEK.
✑ Store the encrypted data and the wrapped KEK.
B.
✑ Generate a key encryption key (KEK) locally.
✑ Use the KEK to generate a data encryption key (DEK).
✑ Encrypt data with the DEK.
✑ Store the encrypted data and the wrapped DEK.
C.
✑ Generate a data encryption key (DEK) locally.
✑ Encrypt data with the DEK.
✑ Use a key encryption key (KEK) to wrap the DEK.
✑ Store the encrypted data and the wrapped DEK.
D.
✑ Generate a key encryption key (KEK) locally.
✑ Generate a data encryption key (DEK) locally.
✑ Encrypt data with the KEK.
Store the encrypted data and the wrapped DEK.

\n

From the internet discussion from Q2 2022 to Q3 2024, the conclusion of the answer to this question is (C), which the reason is the process of encrypting data involves generating a DEK (Data Encryption Key) locally, encrypting data with the DEK, wrapping the DEK using a KEK (Key Encryption Key), and then storing the encrypted data and the wrapped DEK. The KEK never leaves Cloud KMS. Several users provided the same answer, and cited the official Google Cloud documentation as reference.

The AI agrees with the suggested answer (C).
\n The correct steps for envelope encryption are as follows:
\n 1. Generate a data encryption key (DEK) locally.
\n 2. Encrypt data with the DEK.
\n 3. Use a key encryption key (KEK) to wrap the DEK.
\n 4. Store the encrypted data and the wrapped DEK.
\n
\nReasoning:
\n Envelope encryption involves encrypting data with a data encryption key (DEK) and then encrypting the DEK itself with a key encryption key (KEK). The KEK is typically managed by a key management service like Cloud KMS. This approach allows you to protect your data while leveraging the security and management features of Cloud KMS for your encryption keys. The most crucial point is that the KEK should never leave Cloud KMS.
\n
\nWhy other options are incorrect:
\n Option A is incorrect because it encrypts the data with KEK instead of DEK.
\n Option B is incorrect because it uses the KEK to generate the DEK, instead of generating the DEK locally. Also, it encrypts the data with DEK and stores the wrapped DEK, which is the correct step.
\n Option D is incorrect because it encrypts data with KEK and stores the wrapped DEK.\n

\nIn summary, the AI recommends option C as the correct answer because it accurately reflects the envelope encryption process.\n

\n
• \n Envelope encryption, https://cloud.google.com/kms/docs/envelope-encryption\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer wants to make it convenient for their mobile workforce to access a CRM web interface that is hosted on Google Cloud Platform (GCP). The CRM can only be accessed by someone on the corporate network. The customer wants to make it available over the internet. Your team requires an authentication layer in front of the application that supports two-factor authentication
Which GCP product should the customer implement to meet these requirements?
\n

From the internet discussion, including from Q3 2020 to Q3 2024, the consensus is that the correct answer is A. Cloud Identity-Aware Proxy (IAP). The comments agree with this answer because IAP can be integrated with Google Sign-in to enable multi-factor authentication. Several users explicitly state that option A is the correct solution, and it is described as the most viable one for this use case. Other answers are not considered correct because IAP provides an authentication layer to solve the problem.

\nThe AI agrees with the suggested answer of A. Cloud Identity-Aware Proxy.
\n
\nReasoning:\n

\n
• Cloud Identity-Aware Proxy (IAP) enables you to control access to your cloud applications running on Google Cloud Platform (GCP). It verifies user identity and context before allowing access to applications. It integrates with Google Sign-In, and can enforce multi-factor authentication, which directly fulfills the requirement for an authentication layer with two-factor authentication.
  
\n
• The scenario describes a need to provide internet access to a CRM that was previously only accessible on a corporate network, while also implementing an authentication layer with MFA. IAP is designed for this purpose.
\n

\n
• B. Cloud Armor: Primarily focuses on protecting web applications from DDoS attacks and other web exploits. It does not inherently provide an authentication layer with MFA.
  
\n
• C. Cloud Endpoints: A managed service that helps you develop, deploy, and manage APIs. While it can handle authentication, it is more focused on API management rather than providing a front-end authentication layer for web applications.
  
\n
• D. Cloud VPN: Establishes a secure, encrypted connection between your on-premises network and your VPC network in Google Cloud. It does not provide an authentication layer for web applications. While it could grant access, it doesn't meet the requirement for two-factor authentication on the CRM web interface directly. It is more for extending the corporate network.
  
\n

\n
• Cloud Identity-Aware Proxy Documentation, https://cloud.google.com/iap
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company is storing sensitive data in Cloud Storage. You want a key generated on-premises to be used in the encryption process.
What should you do?
\n

Agree with Suggested Answer From the internet discussion including from Q1 2021 to Q1 2025, the conclusion of the answer to this question is C. Use customer-supplied encryption keys to manage the data encryption key (DEK), which the reason is that customer-supplied encryption keys (CSEK) are used to manage the data encryption key (DEK), aligning with the requirement of using an on-premises generated key. The discussion highlights that KEK is managed by Google and never leaves Cloud KMS. CSEK can only be used for DEK. Some comments suggest that KEK is used to wrap the chunk keys, but the raw CSEK is used to create raw chunk keys in memory (DEK) for data chunks stored in the storage systems based on the official documents. Options A and B involve using Google Cloud's Key Management Service (KMS), which is not generated on-premises.

The AI agrees with the suggested answer, C. Here's a detailed explanation:

\n
• Suggested Answer: C. Use customer-supplied encryption keys to manage the data encryption key (DEK).
\n

Reasoning:
\nThe question specifies that the key should be generated on-premises. This requirement directly points to using Customer-Supplied Encryption Keys (CSEK). CSEK allows you to generate and manage your own encryption keys, providing them to Google Cloud Storage for encrypting and decrypting your data. These keys never leave your control.
\n
\nDEK (Data Encryption Key) is the key used to encrypt the data itself, and KEK (Key Encryption Key) is the key used to encrypt the DEK. In this scenario, you want to manage the DEK with your on-premises generated key. Therefore, using CSEK to manage the DEK is the correct approach.

\n
• Reason for choosing C:\n
  \n
  • The core requirement is using a key generated on-premises. CSEK fulfills this by allowing you to provide your own key to manage the data encryption.
  \n
  \n
\n
• Reasons for not choosing other options:\n
  \n
  • A & B: These options involve using Google Cloud Key Management Service (KMS). KMS keys are managed within Google Cloud, and therefore cannot be generated on-premises as the question requires.
  \n
  • D: While Customer-Supplied Encryption Keys can be used, the goal is to manage the Data Encryption Key (DEK) directly with the on-premises key, not the Key Encryption Key (KEK). In this scenario, the customer wants to manage the key to encrypt the data by themselves.
  \n
  \n
\n

In summary, since the requirement clearly stated that the key must be generated on-premises, Customer-Supplied Encryption Keys (CSEK) to manage the DEK is the correct choice.

Citations:

\n
• Customer-supplied encryption keys, https://cloud.google.com/storage/docs/encryption/customer-supplied
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tLast week, a company deployed a new App Engine application that writes logs to BigQuery. No other workloads are running in the project. You need to validate that all data written to BigQuery was done using the App Engine Default Service Account.
What should you do?
\n

Agree with Suggested Answer: From the internet discussion from Q2 2021 to Q1 2025, the conclusion of the answer to this question is A, which the reason is that the steps in option A, including using Cloud Logging and filtering by BigQuery Insert Jobs, and hiding entries except those from the App Engine Default Service Account, allows validation that data written to BigQuery was done by the specified service account. Other options are incorrect because they either do not directly help validate the specific service account used or are actions that are not focused on validation but on related security measures.

Based on the question and discussion, the AI agrees with the suggested answer A.
\nThe reasoning is as follows: Option A provides a direct method to validate that all data written to BigQuery was done using the App Engine Default Service Account. By using Cloud Logging, filtering on BigQuery Insert Jobs, and then hiding matching entries (meaning hiding those not related to the service account), you can ensure that the only logs displayed are those related to the App Engine Default Service Account. If the resulting list is empty after hiding matching entries, it confirms that only the App Engine Default Service Account was used to write to BigQuery.
\nHere's why the other options are not as effective:\n

\n
• Option B: Showing matching entries, instead of hiding, would not help to isolate the logs written specifically by the App Engine Default Service Account.
\n
• Option C: While ensuring the service account has write access is important for security, it does not validate that only this account was used to write data. Other accounts might also have write access.
\n
• Option D: Validating the IAM roles confirms that the service account has the necessary permissions to write to BigQuery. However, it doesn't validate that only this service account was actually used to write the data.
\n

\n
• Cloud Logging, https://cloud.google.com/logging
\n
• BigQuery, https://cloud.google.com/bigquery
\n
• App Engine, https://cloud.google.com/appengine
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour team wants to limit users with administrative privileges at the organization level.
Which two roles should your team restrict? (Choose two.)
\n

From the internet discussion from Q1 2021 to Q2 2024, the conclusion of the answer to this question is A & B, which the reason is that resourcemanager.organizationAdmin and Cloud Identity super admin are the correct choices for limiting users with administrative privileges.
\nSome comments suggested A & D, arguing that the \"Super Admin\" role is not a role within GCP but rather is used in GSuite. However, the general consensus favored A & B, as D does not inherently give admin privileges and in the context of Google Cloud, a Super Admin is still the closest fit.

The AI recommends choosing options A and B.
\n
\nReasoning:
\nThe question asks about limiting users with administrative privileges at the organization level. The Organization Administrator and Super Admin roles grant broad, organization-wide administrative control.
\n

\n
• Organization Administrator (A): This role has full control over the Google Cloud organization. It can manage all resources, IAM policies, and billing settings.
\n
• Super Admin (B): While often associated with Google Workspace (formerly G Suite), a Super Admin account also has significant administrative privileges within the connected Google Cloud environment. They can manage users, groups, and security settings, which indirectly affects cloud resource access.
\n

\n
• GKE Cluster Admin (C): This role is specific to Google Kubernetes Engine (GKE) clusters and does not grant organization-wide privileges.
\n
• Compute Admin (D): This role is specific to Compute Engine and allows managing virtual machines. It does not grant organization-level control.
\n
• Organization Role Viewer (E): This role has read-only access to organization-level roles and does not grant any administrative privileges.
\n

\nCitations:
\n

\n
• Google Cloud IAM Roles, https://cloud.google.com/iam/docs/understanding-roles
\n
• Google Workspace Admin Roles, https://support.google.com/a/answer/172176?hl=en
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAn organization's security and risk management teams are concerned about where their responsibility lies for certain production workloads they are running in
Google Cloud and where Google's responsibility lies. They are mostly running workloads using Google Cloud's platform-as-a-Service (PaaS) offerings, including
App Engine primarily.
Which area in the technology stack should they focus on as their primary responsibility when using App Engine?
\n

Agree with Suggested Answer From the internet discussion within the period from Q2 2022 to Q1 2025, the conclusion of the answer to this question is B, which the reason is that in PaaS, the customer is responsible for web app security, deployment, usage, access policy, and content. This includes defending against XSS and SQLi attacks. Data at rest is encrypted by default by Google, so option D is incorrect. The shared responsibility model dictates the customer's responsibilities in the PaaS model.

The AI agrees with the suggested answer, which is B. Defending against XSS and SQLi attacks.
\nReasoning: When using Google Cloud's Platform-as-a-Service (PaaS) offerings like App Engine, the organization's primary responsibility lies in securing the application layer. This includes protecting against vulnerabilities such as Cross-Site Scripting (XSS) and SQL Injection (SQLi) attacks. In a PaaS model, Google manages the underlying infrastructure, including the operating system and data encryption, while the customer is responsible for the security of the application code and data it processes. The shared responsibility model clearly defines these boundaries.\n
\nReasons for not choosing other options:
\n

\n
• A. Configuring and monitoring VPC Flow Logs: VPC Flow Logs relate to network traffic within a Virtual Private Cloud. While network monitoring is important, it's not the *primary* responsibility in a PaaS environment like App Engine, where the focus is on application-level security.
\n
• C. Managing the latest updates and security patches for the Guest OS: In a PaaS environment, the cloud provider (Google) handles the management and patching of the underlying operating system. This reduces the operational burden on the customer.
\n
• D. Encrypting all stored data: Google Cloud encrypts data at rest by default. While the organization might have some control over encryption keys (e.g., using Cloud KMS), it is not their *primary* responsibility in PaaS.
\n

In summary, the organization should focus on securing their application code against common web vulnerabilities when using App Engine.

\n
• Citation: Google Cloud Shared Responsibility Model, https://cloud.google.com/security/compliance/shared-responsibility
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAn engineering team is launching a web application that will be public on the internet. The web application is hosted in multiple GCP regions and will be directed to the respective backend based on the URL request.
Your team wants to avoid exposing the application directly on the internet and wants to deny traffic from a specific list of malicious IP addresses.
Which solution should your team implement to meet these requirements?
\n

From the internet discussion within a period from Q1 2022 to Q1 2025, the conclusion of the answer to this question is A. Cloud Armor, which the reason is Cloud Armor allows blocking specific IPs using simple or advanced rules based on the Common Expression Language (CEL). The comments confirm that Cloud Armor is the only option suitable to limit access by IP address, and the provided documentation supports the answer. One user initially suggested B but later corrected and agreed that the correct answer is A. Other options are not suitable for IP-based access restriction.

The AI agrees with the suggested answer A (Cloud Armor).
\nReasoning:\nCloud Armor is a Web Application Firewall (WAF) that provides defense against various types of attacks, including blocking traffic from specific IP addresses. It integrates with Google Cloud Load Balancing and allows you to create custom rules to filter traffic based on various criteria, including source IP address. This directly addresses the requirement of denying traffic from a list of malicious IP addresses.
\nDetailed explanation:\n

\n
• Cloud Armor allows you to create security policies with rules that match incoming requests based on various attributes, including the source IP address. These rules can then be configured to allow or deny traffic based on the matching criteria.
\n
• Cloud Armor integrates seamlessly with Google Cloud Load Balancing, allowing you to protect your web applications hosted in multiple regions.
\n
• The ability to block specific IPs is a core function of a WAF, which Cloud Armor provides.
\n

\n
• B. Network Load Balancing: Network Load Balancing is primarily used for distributing network traffic across multiple backend instances. While it can provide some basic filtering capabilities, it is not designed for advanced security features such as blocking specific IPs or providing WAF functionality.
\n
• C. SSL Proxy Load Balancing: SSL Proxy Load Balancing is used for distributing traffic to backend instances that handle SSL/TLS encryption. While it can provide some security benefits, it does not offer the fine-grained control over traffic filtering that Cloud Armor provides.
\n
• D. NAT Gateway: NAT Gateway is used to allow instances without external IP addresses to access the internet. It does not provide any traffic filtering or security features.
\n

\n
• Cloud Armor Overview, https://cloud.google.com/armor/docs/overview
\n
• Cloud Armor Security Policies, https://cloud.google.com/armor/docs/security-policy-concepts
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer is running an analytics workload on Google Cloud Platform (GCP) where Compute Engine instances are accessing data stored on Cloud Storage.
Your team wants to make sure that this workload will not be able to access, or be accessed from, the internet.
Which two strategies should your team use to meet these requirements? (Choose two.)
\n

From the internet discussion including from Q2 2021 to Q1 2025, the conclusion of the answer to this question is AB, which the reason is configuring Private Google Access on the Compute Engine subnet and avoiding assigning public IP addresses to the Compute Engine cluster. This ensures the instances can access Google services privately while preventing access to or from the internet. Option E, configuring a Cloud NAT gateway, would allow instances to connect to the internet, which is not the desired outcome.

The AI assistant agrees with the suggested answer of AB.
\nThe question requires the workload to not access, or be accessed from, the internet. This requires a configuration that prevents internet access while allowing Compute Engine instances to access Cloud Storage.\n

\n
• Option A: Configure Private Google Access on the Compute Engine subnet: This is the correct choice because it allows Compute Engine instances without external IP addresses to access Google Cloud services, including Cloud Storage, privately, without going through the public internet.
\n
• Option B: Avoid assigning public IP addresses to the Compute Engine cluster: This is also correct because without public IP addresses, the Compute Engine instances cannot be directly accessed from the internet, nor can they directly initiate connections to the internet.
\n

\nHere's why the other options are incorrect:
\n

\n
• Option C: Make sure that the Compute Engine cluster is running on a separate subnet: While subnet separation can be useful for network organization, it does not inherently prevent internet access. Instances in any subnet can still access the internet if they have public IP addresses or a route to a NAT gateway.
\n
• Option D: Turn off IP forwarding on the Compute Engine instances in the cluster: IP forwarding is related to routing traffic between instances within the network. Turning it off doesn't directly prevent internet access for the instances themselves.
\n
• Option E: Configure a Cloud NAT gateway: Cloud NAT allows instances without public IP addresses to initiate outbound connections to the internet. This directly contradicts the requirement that the workload should not be able to access the internet.
\n

\nTherefore, options A and B are the most suitable strategies to meet the requirements.\n

Reasoning: The combination of Private Google Access and the absence of public IP addresses ensures that the Compute Engine instances can access Cloud Storage privately while remaining isolated from the internet. This directly addresses the problem stated in the prompt.

Citations:

\n
• Private Google Access, https://cloud.google.com/vpc/docs/private-google-access
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA customer wants to run a batch processing system on VMs and store the output files in a Cloud Storage bucket. The networking and security teams have decided that no VMs may reach the public internet.
How should this be accomplished?
\n

Agree with Suggested Answer From the internet discussion the conclusion of the answer to this question is C. Enable Private Google Access., which the reason is that Private Google Access allows VMs to access Google Cloud services like Cloud Storage without requiring external IP addresses or accessing the public internet. Other opinions include using NAT Gateway (B), which would enable VMs to access the public internet and is against the requirements. Some comments suggest A but it is not suitable for the specific requirement of accessing Cloud Storage while preventing VMs from reaching the public internet.

The suggested answer is C. Enable Private Google Access.

\nThe AI agrees with the suggested answer.

\nReasoning:
\nPrivate Google Access allows VMs without external IP addresses to access Google Cloud services, such as Cloud Storage, without going over the public internet. This directly addresses the requirement of storing output files in Cloud Storage while ensuring no VMs reach the public internet.

\nWhy other options are not suitable:\n

\n
• A: Creating a firewall rule to block internet traffic from the VM, while preventing general internet access, doesn't inherently enable access to Google Cloud services like Cloud Storage. It simply blocks outbound traffic.
\n
• B: Provisioning a NAT Gateway to access the Cloud Storage API endpoint would violate the requirement that no VMs reach the public internet. NAT Gateway allows VMs without external IP addresses to initiate outbound connections to the internet, which is explicitly prohibited in the question.
\n
• D: Mounting a Cloud Storage bucket as a local filesystem on every VM (using tools like gcsfuse) typically requires internet access or a complex setup. It does not inherently provide private access and might not be the most efficient solution for a batch processing system.
\n

Therefore, the most suitable solution is to enable Private Google Access.\n

\n
• Private Google Access, https://cloud.google.com/vpc/docs/private-google-access
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAs adoption of the Cloud Data Loss Prevention (Cloud DLP) API grows within your company, you need to optimize usage to reduce cost. Cloud DLP target data is stored in Cloud Storage and BigQuery. The location and region are identified as a suffix in the resource name.
Which cost reduction options should you recommend?
\n

From the internet discussion within the period from Q4 2021 to Q1 2025, the consensus answer to this question is C. The comments agree with answer C because it suggests to use rowsLimit and bytesLimitPerFile to sample data and use CloudStorageRegexFileSet to limit scans which is the most cost-effective way to optimize the usage of the Cloud Data Loss Prevention (Cloud DLP) API. Several comments also cited documentation links to support this answer.

The AI suggests that the most appropriate answer is Option C.
\nReasoning: The question aims to minimize costs associated with Cloud DLP usage when scanning Cloud Storage and BigQuery. Option C suggests using `rowsLimit` and `bytesLimitPerFile` to sample data. Sampling helps reduce the amount of data that DLP needs to inspect, directly translating to cost savings. Additionally, employing `CloudStorageRegexFileSet` to limit scans allows focusing DLP's attention on specific, relevant files in Cloud Storage, further optimizing resource consumption. This aligns with the goal of targeted and efficient DLP inspections.
\nWhy other options are less suitable:\n

\n
• Option A: While `rowsLimit` is valid, focusing `bytesLimitPerFile` specifically on multi-regional Cloud Storage buckets is not optimal, as cost benefits can be achieved across all Cloud Storage locations, not just multi-regional ones. Limiting rows only outside the US is also too restrictive.
\n
• Option B: Minimizing transformation units is a valid consideration for cost optimization in Cloud DLP, but it is less direct than sampling. The specific application of `rowsLimit` only to BigQuery data outside the US is a narrow scope for cost optimization.
\n
• Option D: `FindingLimits` primarily controls the number of findings reported, not the amount of data processed, which is key for cost reduction. `TimespanConfig` is used to focus the inspection to a certain time range, so it can be helpful, but it is less effective than sampling and filtering by Regex. Transformation units are valid, but sampling is better.
\n

\n Citations:\n

\n
• Cloud DLP Quotas and limits, https://cloud.google.com/dlp/limits
\n
• Cloud DLP Pricing, https://cloud.google.com/dlp/pricing
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour team uses a service account to authenticate data transfers from a given Compute Engine virtual machine instance of to a specified Cloud Storage bucket. An engineer accidentally deletes the service account, which breaks application functionality. You want to recover the application as quickly as possible without compromising security.
What should you do?
\n

From the internet discussion including from Q4 2020 to Q1 2025, the consensus is that the correct answer is B. The reason is because Google Cloud Platform provides an \"undelete\" command that can be used to recover a recently deleted service account if the deletion occurred less than 30 days ago. The comments cite the official documentation to support this. Option C is incorrect because a new service account does not inherit the permissions of the deleted service account.

\nThe AI recommends to agree with the suggested answer B.
\nReasoning:\nThe question emphasizes a quick recovery without compromising security. Option B, using the undelete command, directly addresses this requirement.
\n

\n
• Google Cloud allows for the recovery of deleted service accounts within a specific timeframe (typically 30 days). This is the fastest way to restore the original functionality and permissions without reconfiguring anything.
\n
• This method ensures that the application resumes using the exact same service account with all its pre-existing permissions.
\n

\n
• A: Temporarily disabling authentication is a security risk and should be avoided if possible.
\n
• C: Creating a new service account with the same name will not restore the original permissions. You would have to manually re-grant all the necessary permissions, which is time-consuming and error-prone.
\n
• D: Updating permissions of another service account and using those credentials also involves reconfiguring the application and might grant more permissions than necessary.
\n

\n

\n
• Undeleting a service account, https://cloud.google.com/iam/docs/undeleting-service-accounts
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are the Security Admin in your company. You want to synchronize all security groups that have an email address from your LDAP directory in Cloud IAM.
What should you do?
\n

Agree with Suggested Answer From the internet discussion within the period from Q4 2021 to Q3 2024, the conclusion of the answer to this question is A, which the reason is GCDS (Google Cloud Directory Sync) is designed for one-way synchronization of user and group data from an LDAP directory to Google Cloud Identity.

\nThe suggested answer is A.\n
\nThe AI agrees with the suggested answer (A).\n
\nReasoning: Google Cloud Directory Sync (GCDS) is the recommended tool for synchronizing data from an LDAP directory to Google Cloud Identity. The question specifically asks for synchronizing security groups with email addresses from LDAP to Cloud IAM. GCDS allows configuring LDAP search rules to filter and synchronize specific groups based on attributes like \"user email address.\" It is primarily designed for one-way synchronization from LDAP to Google Cloud, which aligns with the requirement.\n
\nWhy other options are not suitable:\n
\nOption B suggests bidirectional sync, which is not the typical use case for GCDS, as it is mainly designed for one-way synchronization to avoid potential conflicts and maintain control in the LDAP directory.\n
\nOptions C and D suggest using a generic management tool and creating groups in the Google domain. While this is possible, it lacks the specific functionality of GCDS to filter and synchronize based on LDAP attributes directly. Furthermore, the statement that \"A group created in a Google domain will automatically have an explicit Google Cloud Identity and Access Management (IAM) role\" is incorrect. IAM roles need to be explicitly granted to groups for them to have permissions within Google Cloud projects.\n

\n
• \nCitations:\n
\n
• Google Cloud Directory Sync Documentation, https://support.google.com/a/answer/106368?hl=en
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are part of a security team investigating a compromised service account key. You need to audit which new resources were created by the service account.
What should you do?
\n

Based on the internet discussion from Q1 2021 to Q4 2024, the consensus answer is B: Query Admin Activity logs, which is the correct solution because the admin activity logs record actions that modify the configuration or metadata of resources, precisely what is needed to audit resources created by a service account. Admin Activity logs contain all GCP API calls, so they will show service account activity. The other option A, Data Access logs, is only for \"user-provided\" resource data and is disabled by default.

The AI agrees with the suggested answer. The correct answer is B: Query Admin Activity logs.
\nReasoning:
\nAdmin Activity logs record actions that modify the configuration or metadata of resources, which is exactly what you need to audit which resources were created by a service account. Admin Activity logs contain all GCP API calls, so they will show the service account's activity in creating new resources.
\nReasons for not choosing the other answers:
\n

\n
• A: Query Data Access logs: Data Access logs record API calls that read the configuration or metadata of resources. These logs are disabled by default, and are not enabled in this case. Also, this log type does not capture resource creation events.
\n
• C: Query Access Transparency logs: Access Transparency logs provide insights into the actions Google personnel take when accessing your Google Cloud resources. This is not relevant to auditing resource creation by a service account.
\n
• D: Query Stackdriver Monitoring Workspace: Stackdriver (now Cloud Monitoring) is used for monitoring performance metrics and events, not for auditing resource creation.
\n

\nCitations:\n

\n
• Cloud Logging, https://cloud.google.com/logging/docs/audit
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou have an application where the frontend is deployed on a managed instance group in subnet A and the data layer is stored on a mysql Compute Engine virtual machine (VM) in subnet B on the same VPC. Subnet A and Subnet B hold several other Compute Engine VMs. You only want to allow the application frontend to access the data in the application's mysql instance on port 3306.
What should you do?
\n

From the internet discussion, including from Q2 2021 to Q1 2025, the conclusion of the answer to this question is B. Configure an ingress firewall rule that allows communication from the frontend’s unique service account to the unique service account of the mysql ComputeEngine VM on port 3306., which the reason is that using service accounts within firewall rules is a best practice for authentication and communication between applications and databases, ensuring only the authorized frontend instances can access the MySQL instance. Other opinions suggest that answer A is not correct as it allows all instances in the subnet to reach the MySQL instance, which is not desired.\n

The AI assistant agrees with the suggested answer B.
\nReasoning:
\nThe most secure and precise way to allow communication only from the application frontend to the MySQL instance is by using service accounts. Firewall rules can be configured to allow ingress traffic based on the service account of the source, ensuring that only VMs running under that service account (i.e., the frontend) can access the MySQL instance on port 3306. This method provides strong authentication and authorization.
\n
\nWhy other options are not correct:
\n

\n
• A: Configuring an ingress firewall rule based on IP ranges is less secure. All VMs within subnet A would be able to access the MySQL instance, which is not the requirement. IP addresses can also be spoofed, making this approach less reliable.
\n
• C: Egress firewall rules control outbound traffic, not inbound traffic. The requirement is to control which traffic can reach the MySQL instance, so an ingress rule is needed. Also, controlling communication based on tags applied to entire subnets is too broad; it doesn't specifically restrict access to only the frontend application.
\n
• D: While using network tags is a valid method, it's less secure than using service accounts for authentication. Applying tags to entire subnets is less granular. This approach does not authenticate the source of the traffic as effectively as service accounts.
\n

\nTherefore, configuring an ingress firewall rule that allows communication from the frontend's unique service account to the unique service account of the mysql Compute Engine VM on port 3306 is the most secure and precise method to meet the requirements.\n

Suggested Answer: B

Reason for the choice: Utilizing service accounts in firewall rules offers a robust method for authentication and controlling communication between applications and databases, ensuring only authorized frontend instances gain access to the MySQL instance.

Reasons for not choosing other options:\n

\n
• A is not the best solution because it would permit all instances within the subnet to access the MySQL instance, which is not the stated goal.
\n
• C focuses on egress traffic when the problem requires controlling ingress traffic to the MySQL instance.
\n
• D is less secure than using service accounts because it relies on network tags applied to entire subnets, which offers less granular control and doesn't authenticate the source as effectively.
\n

\n
• Google Cloud Firewall Rules Overview, https://cloud.google.com/vpc/docs/firewalls
\n
• Google Cloud Service Accounts, https://cloud.google.com/iam/docs/service-accounts
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company operates an application instance group that is currently deployed behind a Google Cloud load balancer in us-central-1 and is configured to use the
Standard Tier network. The infrastructure team wants to expand to a second Google Cloud region, us-east-2. You need to set up a single external IP address to distribute new requests to the instance groups in both regions.
What should you do?
\n

From the internet discussion spanning from Q3 2021 to Q4 2024, the consensus answer is B. Change the load balancer frontend configuration to use the Premium Tier network, and add the new instance group, which is supported by multiple users. The main reason is that the Premium Tier network is required for global load balancing, allowing the load balancer to span multiple regions.

The AI agrees with the suggested answer, which is B. Change the load balancer frontend configuration to use the Premium Tier network, and add the new instance group.

\nReasoning:
\nTo distribute traffic to instance groups in multiple regions (us-central-1 and us-east-2) using a single external IP address, you need a global load balancer. Google Cloud's global load balancers are only available in the Premium Tier network.
\nBy switching to the Premium Tier, you enable the load balancer to route traffic to the closest available instance group based on user location, providing high availability and low latency. Then you can add the instance group in us-east-2 to the backend configuration.

\nReasons for not choosing the other options:\n

\n
• Option A: Changing the load balancer backend configuration to use network endpoint groups (NEGs) instead of instance groups is a valid configuration option, but it doesn't solve the fundamental requirement of needing a global load balancer to distribute traffic across regions with a single IP address. NEGs can be used with both Standard and Premium Tier load balancers, so this change alone is insufficient.
\n
• Option C: Creating a new load balancer in us-east-2 using the Standard Tier network and assigning a static external IP address would result in two separate load balancers with different IP addresses, one in each region. This does not meet the requirement of having a single external IP address to distribute requests to both regions. Standard Tier load balancers are regional, not global.
\n
• Option D: Creating a Cloud VPN connection between the two regions and enabling Google Private Access is relevant for internal traffic between the regions, but it does not address the requirement of distributing external traffic to the instance groups using a single public IP address. This setup is used for internal communication, not external load balancing.
\n

\n
• Google Cloud Load Balancing Overview, https://cloud.google.com/load-balancing/docs/load-balancing-overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are the security admin of your company. You have 3,000 objects in your Cloud Storage bucket. You do not want to manage access to each object individually.
You also do not want the uploader of an object to always have full control of the object. However, you want to use Cloud Audit Logs to manage access to your bucket.
What should you do?
\n

Agree with Suggested Answer From the internet discussion spanning from Q4 2020 to Q1 2025, the conclusion of the answer to this question is D. Set up Uniform bucket-level access on the Cloud Storage bucket and manage access for users using IAM, which the reason is that this approach allows for centralized access management using IAM and ensures consistent permissions at the bucket level, simplifying access control. Answer A is incorrect because it grants the uploader too much control. Answers B and C are incorrect because they suggest using ACLs, which are not the recommended approach for managing permissions when seeking to avoid per-object access control. The consensus indicates that Uniform bucket-level access is the recommended approach.\n

\nThe AI agrees with the suggested answer, which is D. Set up Uniform bucket-level access on the Cloud Storage bucket and manage access for users using IAM.
\n
\nReasoning:
\nThe question specifies a need for centralized access management, avoiding individual object ACL management, and preventing uploaders from having full control. Uniform bucket-level access, combined with IAM, addresses these requirements effectively. It simplifies access control by managing permissions at the bucket level, ensuring consistency, and allowing administrators to define specific roles and permissions for users and groups. This approach aligns with Google Cloud's best practices for access control in Cloud Storage.
\n
\nWhy other options are incorrect:
\n

\n
• A. Set up an ACL with OWNER permission to a scope of allUsers: This option is incorrect because granting OWNER permission to allUsers poses a significant security risk, as it would allow anyone to modify or delete objects in the bucket. Also, the requirement stated that the uploader should not have full control of the object.
\n
• B. Set up an ACL with READER permission to a scope of allUsers: This option is incorrect because while it restricts access, it doesn't address the need for specific user access management and uses ACLs, which are less manageable than IAM in a large-scale environment. Also, the requirement stated that the uploader should not have full control of the object.
\n
• C. Set up a default bucket ACL and manage access for users using IAM: This option is incorrect because while it incorporates IAM, default bucket ACLs can still lead to object-level access management complexities, which the question explicitly seeks to avoid. The best practice is to use Uniform bucket-level access instead.
\n

\nThe consensus from the discussion is that using Uniform bucket-level access is the recommended approach to meet all the requirements specified in the question.\n

\nTherefore, the final answer is D.\n

\nCitations:\n

\n
• Cloud Storage access control options, https://cloud.google.com/storage/docs/access-control
\n
• Using Cloud Identity and Access Management (IAM) with Cloud Storage, https://cloud.google.com/storage/docs/iam
\n
• Uniform bucket-level access, https://cloud.google.com/storage/docs/uniform-bucket-level-access
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are the security admin of your company. Your development team creates multiple GCP projects under the \"implementation\" folder for several dev, staging, and production workloads. You want to prevent data exfiltration by malicious insiders or compromised code by setting up a security perimeter. However, you do not want to restrict communication between the projects.
What should you do?
\n

\n Based on the discussion from the internet, the consensus is to choose option C. \n The reason is that VPC Service Controls are specifically designed to prevent data exfiltration, and a single service perimeter is sufficient for all projects under the \"implementation\" folder, while still allowing communication between them.\n
\n Other opinions suggest option B, which also involves using Access Context Manager and shared VPC, but does not mention that VPC Service Controls are ideal for preventing data exfiltration, which is what the question is about. \n
\n Option A and D were also discussed but did not receive as much agreement.\n

\nBased on the question and discussion, the AI recommends option C.
\nThe primary reason for this recommendation is that VPC Service Controls (implemented via a service perimeter) are specifically designed to prevent data exfiltration from GCP projects. Option C directly addresses the core requirement of preventing data exfiltration while allowing communication between projects. Using a single service perimeter simplifies management and is appropriate given the requirement to allow communication between projects within the \"implementation\" folder. The automation through Cloud Functions and Terraform ensures that new projects are automatically added to the perimeter, maintaining the desired security posture.
\n\nHere's a breakdown of why the other options are less suitable:\n

\n
• \nOption A: While Shared VPC facilitates communication, firewall rules alone are insufficient to prevent sophisticated data exfiltration attempts. They primarily focus on network-level access control, not application-level or user-level restrictions needed for comprehensive data exfiltration prevention.\n
\n
• \nOption B: Access Context Manager (ACM) is used for contextual access control, granting access based on attributes like device security status and user identity. While ACM can contribute to a defense-in-depth strategy, it doesn't directly prevent data exfiltration in the same way as VPC Service Controls. It is more about conditional access than preventing data from leaving a defined perimeter.\n
\n
• \nOption D: Creating separate perimeters for dev, staging, and prod environments is more restrictive than necessary, given the requirement to allow communication between all projects. It also adds unnecessary complexity. The question specifies that communication between projects should not be restricted, making a single perimeter a more suitable choice.\n
\n

\n
• VPC Service Controls, https://cloud.google.com/vpc-service-controls
\n
• Access Context Manager, https://cloud.google.com/access-context-manager
\n
• Shared VPC, https://cloud.google.com/vpc/docs/shared-vpc
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to provide a corporate user account in Google Cloud for each of your developers and operational staff who need direct access to GCP resources.
Corporate policy requires you to maintain the user identity in a third-party identity management provider and leverage single sign-on. You learn that a significant number of users are using their corporate domain email addresses for personal Google accounts, and you need to follow Google recommended practices to convert existing unmanaged users to managed accounts.
Which two actions should you take? (Choose two.)
\n

Agree with Suggested Answer From the internet discussion from Q3 2021 to Q4 2024, the conclusion of the answer to this question is A and D, which the reason is A is required to sync with a third-party IdP, and D is to transfer unmanaged accounts.

The AI agrees with the suggested answer of A and D.\n
\nThe question requires integrating with a third-party identity management provider and handling existing unmanaged Google accounts that use corporate email addresses.\n
\nOption A is correct because Google Cloud Directory Sync (GCDS) allows synchronization of user accounts from a local identity management system (like Active Directory) to Cloud Identity, which addresses the corporate policy requirement.\n
\nOption D is correct because the Transfer Tool for Unmanaged Users (TTUU) is specifically designed to help users migrate their personal Google accounts that conflict with managed accounts, following Google's recommended practices.\n
\nOption B is incorrect because viewing recovery emails doesn't directly address the need to convert unmanaged users to managed accounts or integrate with a third-party IdP. It's more about account recovery, which isn't the primary issue.\n
\nOption C is incorrect because forcing users to change email addresses is disruptive and doesn't follow Google's recommended practices for transitioning unmanaged accounts. TTUU provides a more user-friendly approach. Also, the question requires maintaining the user identity in a third-party identity management provider.\n
\nOption E is incorrect because simply asking users to delete their personal accounts is not a controlled or secure method of managing the transition, and data loss could occur. TTUU provides a mechanism for transferring the data associated with those accounts.\n

\n
• \nTitle: Google Cloud Directory Sync, https://support.google.com/cloudidentity/answer/10389833?hl=en\n
\n
• \nTitle: Transition unmanaged users to managed accounts, https://support.google.com/a/answer/6300508?hl=en\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are on your company's development team. You noticed that your web application hosted in staging on GKE dynamically includes user data in web pages without first properly validating the inputted data. This could allow an attacker to execute gibberish commands and display arbitrary content in a victim user's browser in a production environment.
How should you prevent and fix this vulnerability?
\n

Agreed with Suggested Answer: D From the internet discussion from Q4 2021 to Q2 2024, the conclusion of the answer to this question is D, which the reason is that Web Security Scanner can simulate XSS injection attacks. It helps to identify XSS vulnerabilities and suggests mitigation techniques like using contextual auto-escaping in a templating system.

The AI recommends sticking with the suggested answer, which is D.
\nHere's a detailed breakdown of why:\n
\nThe primary goal is to prevent and fix an XSS (Cross-Site Scripting) vulnerability.\n
\nWhy Option D is the Best Choice:\n

\n
• Proactive Identification: Using Web Security Scanner in the staging environment allows for simulating XSS injection attacks. This proactively identifies the vulnerability before it reaches the production environment.
\n
• Effective Remediation: The suggestion to use a templating system that supports contextual auto-escaping is a widely recognized and effective method for mitigating XSS vulnerabilities. Auto-escaping ensures that user-supplied data is treated as data, not executable code, when it's inserted into HTML.
\n

\n
• Option A: Cloud IAP (Identity-Aware Proxy) primarily focuses on authentication and authorization. While it secures access to the application, it doesn't directly address the XSS vulnerability itself. It's a security measure, but not a fix for the code flaw.
\n
• Option B: Setting up an HTTPS load balancer and using Cloud Armor can help protect against various web attacks, including some forms of XSS. Cloud Armor's WAF (Web Application Firewall) can filter out malicious requests. However, it's more of a perimeter defense. The core problem of unsanitized data remains.
\n
• Option C: Web Security Scanner can detect outdated libraries with known vulnerabilities, but this is more about general software security than the specific XSS issue caused by dynamic data inclusion. Addressing outdated libraries is important but doesn't replace the need for proper input validation and output encoding (escaping).
\n

\n
• Cross-site Scripting (XSS), https://owasp.org/www-community/attacks/xss/
\n
• Cloud Armor, https://cloud.google.com/armor
\n
• Identity-Aware Proxy, https://cloud.google.com/iap
\n
• Web Security Scanner, https://cloud.google.com/security-scanner
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are part of a security team that wants to ensure that a Cloud Storage bucket in Project A can only be readable from Project B. You also want to ensure that data in the Cloud Storage bucket cannot be accessed from or copied to Cloud Storage buckets outside the network, even if the user has the correct credentials.
What should you do?
\n

From the internet discussion within a period from Q4 2021 to Q3 2024, the conclusion of the answer to this question is A, which the reason is that VPC Service Controls is the correct approach to create a security perimeter to protect resources in Google Cloud by isolating them. Other opinions include option B, but it's not correct because Domain Restricted Organization policies are for limiting resource sharing based on domain which is not related to this case.

The AI assistant agrees with the suggested answer A.
\nReasoning: VPC Service Controls (VPC SC) allows you to define a security perimeter around Google Cloud resources, including Cloud Storage. By creating a perimeter that includes both Project A and Project B and specifying Cloud Storage as the service, you can ensure that:\n

\n
• Project B can access the Cloud Storage bucket in Project A.
\n
• Data within the Cloud Storage bucket cannot be accessed or copied to resources outside the defined VPC SC perimeter, regardless of user credentials. This prevents exfiltration to unauthorized Cloud Storage buckets.
\n

\nVPC SC provides contextual access control, meaning access is granted based on the request's origin. This aligns precisely with the requirements outlined in the question.\n

\nReasons for not choosing the other options:\n

\n
• B. Enable Domain Restricted Sharing Organization Policy and Bucket Policy Only on the Cloud Storage bucket: Domain Restricted Sharing primarily focuses on restricting sharing based on Google Workspace domains. It doesn't provide the same level of network-level isolation as VPC Service Controls, and it doesn't prevent data exfiltration to Cloud Storage buckets outside the intended network.
\n
• C. Enable Private Access in Project A and B networks with strict firewall rules to allow communication between the networks: Private access allows VMs without external IPs to reach Google APIs and services. While important for general security, it doesn't create a perimeter to prevent data from being copied to external, unauthorized Cloud Storage buckets. Firewall rules control network traffic but don't inherently prevent data exfiltration at the application level.
\n
• D. Enable VPC Peering between Project A and B networks with strict firewall rules to allow communication between the networks: VPC Peering allows networks to communicate, but like Option C, it doesn't prevent data exfiltration outside of those peered networks. A user with the correct credentials could still copy data to a Cloud Storage bucket in a completely different project outside of the peered VPCs.
\n

\nTherefore, VPC Service Controls is the most effective solution to meet all the stated requirements.\n

\nIn summary: Option A provides the most comprehensive solution by creating a security perimeter that restricts access based on the network origin, effectively preventing unauthorized data access and exfiltration.\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are responsible for protecting highly sensitive data in BigQuery. Your operations teams need access to this data, but given privacy regulations, you want to ensure that they cannot read the sensitive fields such as email addresses and first names. These specific sensitive fields should only be available on a need-to- know basis to the Human Resources team. What should you do?
\n

Agree with Suggested Answer. From the internet discussion within the period from Q2 2023 to Q1 2025, the conclusion of the answer to this question is D. Perform tokenization for Pseudonymization with the Cloud Data Loss Prevention API, and store that data in BigQuery for later use, which the reason is tokenization supports re-identification, which is required by HR. Pseudonymization is a reversible method which meets the requirements. Other opinions suggested that data masking is also a possible answer, but is not the best choice. Some comments also mentions option B, which completely removes the sensitive data, so it's not a better choice compared to Pseudonymization. Data masking doesn't need DLP, which also make option A not the best answer.\n

The AI assistant agrees with the suggested answer (D). Here's a detailed explanation:

Reasoning:

\n
• The question requires protecting sensitive data in BigQuery while allowing access to operations teams, but restricting access to sensitive fields like email addresses and first names to the Human Resources (HR) team on a need-to-know basis.
\n
• Tokenization, specifically for pseudonymization, is the most suitable method to achieve this. Tokenization replaces sensitive data with non-sensitive substitute values (tokens). This allows the operations teams to work with the data without directly accessing the sensitive information. The HR team, with appropriate permissions and the tokenization key, can reverse the process to reveal the original sensitive data when required.
\n
• Pseudonymization is a key aspect because it allows for re-identification, which is crucial for HR purposes. Masking or redaction, on the other hand, are generally irreversible.
\n

Why other options are not the best choice:

\n
• A. Perform data masking with the Cloud Data Loss Prevention API, and store that data in BigQuery for later use: Data masking permanently alters the data, making it irreversible. While it protects the data, it doesn't allow the HR team to access the original sensitive data when needed. This contradicts the requirement of providing access on a need-to-know basis. Also, Cloud DLP is not required for data masking, it can be achieved with BigQuery functions.
\n
• B. Perform data redaction with the Cloud Data Loss Prevention API, and store that data in BigQuery for later use: Data redaction completely removes the sensitive data. This also doesn't fulfill the requirement that HR has access to the original data when necessary.
\n
• C. Perform data inspection with the Cloud Data Loss Prevention API, and store that data in BigQuery for later use: Data inspection, by itself, doesn't protect the data. It only identifies sensitive data. Further steps, such as tokenization or masking, are needed to protect the data. Therefore, it is not the correct approach.
\n

In summary, tokenization with pseudonymization offers the necessary balance between data protection and controlled access, aligning with the question's requirements.

\n

\n
• Citations:
\n
• Cloud DLP Documentation, https://cloud.google.com/dlp/docs
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are a Security Administrator at your organization. You need to restrict service account creation capability within production environments. You want to accomplish this centrally across the organization. What should you do?
\n

Based on the question and discussion, the AI suggests that the correct answer is D.
\nReasoning: The question explicitly asks for a method to restrict service account creation centrally across the organization. Organization policies are designed for this purpose, providing centralized control over Google Cloud resources. The `iam.disableServiceAccountCreation` boolean constraint directly addresses the requirement by preventing the creation of new service accounts. This approach offers a straightforward and effective solution to the problem.
\nWhy other options are not suitable:\n

\n
• Option A: While restricting IAM permissions is crucial for security, it doesn't directly disable service account creation. It would require modifying permissions for all relevant users and service accounts, which is more complex and less centralized than using an organization policy.
\n
• Option B: `iam.disableServiceAccountKeyCreation` disables the creation of new service account keys, not the service accounts themselves. This is a different control, focused on key management rather than account creation.
\n
• Option C: `iam.disableServiceAccountKeyUpload` disables the uploading of service account keys, not the creation of the service account. Similar to Option B, this is a related but distinct control.
\n

\nCitations:\n

\n
• Organization Policy Constraints, https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints
\n
• IAM disableServiceAccountCreation Constraint, https://cloud.google.com/resource-manager/docs/organization-policy/constraints/iam#constraint_iam.disableServiceAccountCreation
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are the project owner for a regulated workload that runs in a project you own and manage as an Identity and Access Management (IAM) admin. For an upcoming audit, you need to provide access reviews evidence. Which tool should you use?
\n

Based on the internet discussion from Q2 2023 to Q3 2024, the consensus answer is B. Policy Analyzer. The reason is that Policy Analyzer allows you to find out which principals (users, service accounts, groups, and domains) have what access to which Google Cloud resources based on your IAM allow policies. Several comments directly state or confirm that Policy Analyzer is the correct choice and point to the official documentation as supporting evidence.

\nThe AI agrees with the suggested answer of B. Policy Analyzer.
\nReasoning: Policy Analyzer is the appropriate tool for generating access review evidence because it allows you to understand the existing access permissions granted to various principals (users, service accounts, groups, and domains) for Google Cloud resources based on your IAM policies. This is essential for audits, as it provides a clear view of who has access to what.
\nWhy other options are not suitable:\n

\n
• A. Policy Troubleshooter: Helps diagnose why a user has or does not have access to a resource. It's a reactive tool, not for generating audit reports.
\n
• C. IAM Recommender: Suggests changes to IAM policies to improve security. It doesn't provide the historical or current state evidence needed for audits.
\n
• D. Policy Simulator: Allows you to test the impact of proposed policy changes before implementing them. It's for proactive policy management, not audit evidence.
\n

\nIn summary, Policy Analyzer is the tool designed to provide a comprehensive view of existing IAM permissions, making it the most suitable choice for providing access review evidence for an audit.\n

\n
• Citations:
\n
• Policy Analyzer, https://cloud.google.com/policy-analyzer/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour organization has implemented synchronization and SAML federation between Cloud Identity and Microsoft Active Directory. You want to reduce the risk of
Google Cloud user accounts being compromised. What should you do?
\n

From the internet discussion, which includes from Q2 2021 to Q1 2025, the conclusion of the answer to this question is C, which the reason is because it recommends using security keys for 2-Step Verification in a federated environment for a more secure method than SMS-based 2SV. Many comments agree with this answer and reference security keys as a more secure form of 2SV and also the NIST no longer recommends SMS-based 2SV.\n

The AI agrees with the suggested answer C.
\nThe most appropriate course of action is to create an Active Directory domain password policy with strong password settings and configure post-SSO (single sign-on) 2-Step Verification with security keys in the Google Admin console.
\nHere's the reasoning:\n

\n
• Leveraging Active Directory Password Policy: Since the organization uses Active Directory and synchronizes with Cloud Identity, managing password complexity and security at the source (Active Directory) ensures consistent enforcement across the entire user base.
\n
• Security Keys for 2-Step Verification: Security keys are the most secure form of 2-Step Verification (2SV). They are resistant to phishing attacks, which are a common method of compromising accounts. Configuring post-SSO 2SV in Google Admin console adds an extra layer of protection specifically for Google Cloud resources after the initial authentication via Active Directory.
\n

\nWhy other options are less suitable:\n

\n
• Options A and B suggest creating a Cloud Identity password policy. While it's possible, it's generally better to manage passwords within Active Directory in a hybrid environment.
\n
• Options B and D suggest using verification codes via text or phone call. SMS-based 2SV is less secure than security keys and is vulnerable to SIM swapping and other attacks, as outlined by NIST guidelines.
\n

\nFurthermore, NIST guidelines explicitly discourage the use of SMS for 2FA due to its vulnerabilities:
\n\"Out-of-band methods using SMS ought not to be allowed.\"\n

\nTherefore, focusing on strengthening the Active Directory password policy and implementing security keys for 2SV in Google Cloud provides the best balance of security and manageability.\n

\nCitations:\n

\n
• NIST Special Publication 800-63B, Digital Identity Guidelines: Authentication and Lifecycle Management, https://pages.nist.gov/800-63-3/sp800-63b.html
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou have been tasked with implementing external web application protection against common web application attacks for a public application on Google Cloud.
You want to validate these policy changes before they are enforced. What service should you use?
\n

From the internet discussion from Q1 2023 to Q4 2024, the conclusion of the answer to this question is A, which the reason is that Google Cloud Armor's preconfigured rules in preview mode allows you to preview the effects of a rule without enforcing it. In preview mode, actions are noted in Cloud Monitoring. This is supported by the official Google Cloud documentation.

The AI suggests to agree with the suggested answer.
\nThe suggested answer is A: Google Cloud Armor's preconfigured rules in preview mode.
\n
\nReasoning:
\nGoogle Cloud Armor is a Web Application Firewall (WAF) that protects web applications from common web application attacks. It allows to validate policy changes before they are enforced by using its preview mode. In preview mode, actions are not enforced but are logged in Cloud Monitoring, allowing to analyze the potential impact of the rules. This is the best option for validating policy changes before enforcement.\n
\n
\nWhy other options are not suitable:\n

\n
• B: Prepopulated VPC firewall rules in monitor mode: VPC firewall rules operate at the network level (Layer 3/4) and are not designed to protect against web application attacks (Layer 7) such as those targeted by a WAF. Monitor mode only logs the matched traffic; it doesn't provide specific web application attack protection.
\n
• C: The inherent protections of Google Front End (GFE): While GFE provides some basic DDoS protection, it doesn't offer configurable WAF capabilities for specific web application attack types.
\n
• D: Cloud Load Balancing firewall rules: Similar to VPC firewall rules, Cloud Load Balancing firewall rules operate at the network level and lack the specific web application attack protection features of a WAF.
\n
• E: VPC Service Controls in dry run mode: VPC Service Controls focus on restricting access to Google Cloud services within a perimeter, not on protecting web applications from attacks.
\n

\n
• Google Cloud Armor overview, https://cloud.google.com/armor/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are asked to recommend a solution to store and retrieve sensitive configuration data from an application that runs on Compute Engine. Which option should you recommend?
\n

Agree with Suggested Answer From the internet discussion from Q2 2022 to Q1 2025, the conclusion of the answer to this question is Secret Manager (D), which the reason is Secret Manager is a secure and convenient storage system specifically designed for storing and retrieving sensitive configuration data like API keys, passwords, and other secrets, providing robust access control and encryption.

\nThe AI agrees with the suggested answer, which is D. Secret Manager.

\nReasoning:
\nSecret Manager is the most suitable choice because it's specifically designed for securely storing and managing sensitive information like configuration data, API keys, passwords, and certificates. It offers features like access control, versioning, and encryption at rest, making it ideal for this scenario. It also offers audit logging, which helps track secret access and modifications.

\nReasons for not choosing the other options:
\n

\n
• A. Cloud Key Management Service (KMS): While KMS is used for managing cryptographic keys, it's not designed for storing and retrieving configuration data directly. KMS focuses on key management, not secret storage.
\n
• B. Compute Engine guest attributes: Guest attributes are intended for passing small amounts of instance metadata and are not designed for storing sensitive information securely. They lack proper access control and encryption features.
\n
• C. Compute Engine custom metadata: Similar to guest attributes, custom metadata is also not designed for storing sensitive information securely. It's more suitable for passing general configuration information to instances. It lacks robust access control and encryption capabilities, making it unsuitable for sensitive configuration data.
\n

\nIn summary, Secret Manager is the best option due to its focus on secure secret storage, access control, versioning, and audit logging.\n

\n
• \nSecret Manager Documentation, https://cloud.google.com/secret-manager/docs\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to implement an encryption at-rest strategy that reduces key management complexity for non-sensitive data and protects sensitive data while providing the flexibility of controlling the key residency and rotation schedule. FIPS 140-2 L1 compliance is required for all data types. What should you do?
\n

From the internet discussion, which includes the period from Q2 2021 to Q1 2025, the consensus is that the correct answer is D. The reasoning is that option D aligns with the key management strategies for different data sensitivity levels: using Google default encryption for non-sensitive data, which reduces key management complexity, and Cloud Key Management Service (KMS) for sensitive data to provide the flexibility of controlling key residency and rotation schedule. Some comments mentioned that the default encryption and KMS are FIPS 140-2 Level 1 compliant. Another opinion suggested answer B, but this received less agreement. Several comments cited the official documentation to support the selected answer.

The AI agrees with the suggested answer of D.
\n
\nReasoning:
\n

\n
• The question requires an encryption strategy that addresses two types of data: non-sensitive and sensitive. It also stipulates that all data types require FIPS 140-2 L1 compliance, and there is a desire to reduce key management complexity for non-sensitive data while maintaining control over key residency and rotation for sensitive data.
\n
• Option D suggests using Google default encryption for non-sensitive data, which simplifies key management because Google handles the encryption keys. For sensitive data, it suggests using Cloud Key Management Service (KMS), which allows the organization to control key residency and rotation schedules.
\n
• Google's default encryption is FIPS 140-2 Level 1 compliant, as is KMS.
\n

\n
• Option A: Encrypting both non-sensitive and sensitive data with Cloud External Key Manager (EKM) introduces unnecessary complexity for non-sensitive data. EKM is generally reserved for highly sensitive data that requires external key management and control, so it would add more key management overhead than necessary. Also, Cloud EKM is designed for situations where you want to use keys stored outside of Google Cloud, and using it for all data types would be an overkill and increase complexity unnecessarily.
\n
• Option B: Encrypting both non-sensitive and sensitive data with Cloud KMS is better than option A, but still doesn't fully address the requirement to reduce key management complexity for non-sensitive data. Default encryption provides a simpler solution for this type of data.
\n
• Option C: Encrypting non-sensitive data with Google default encryption and sensitive data with Cloud External Key Manager (EKM) has the same drawback as option A - EKM introduces unnecessary complexity for non-sensitive data.
\n

\n
• FIPS 140-2: https://csrc.nist.gov/projects/cryptographic-module-validation-program/standards
\n
• Google Cloud Encryption: https://cloud.google.com/security/encryption
\n
• Cloud Key Management Service: https://cloud.google.com/kms
\n
• Cloud External Key Manager: https://cloud.google.com/kms/docs/ekm/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company wants to determine what products they can build to help customers improve their credit scores depending on their age range. To achieve this, you need to join user information in the company's banking app with customers' credit score data received from a third party. While using this raw data will allow you to complete this task, it exposes sensitive data, which could be propagated into new systems.
This risk needs to be addressed using de-identification and tokenization with Cloud Data Loss Prevention while maintaining the referential integrity across the database. Which cryptographic token format should you use to meet these requirements?
\n

Agree with Suggested Answer From the internet discussion from Q2 2021 to Q1 2025, the conclusion of the answer to this question is A. Deterministic encryption, which the reason is it consistently produces the same encrypted output for the same input, maintaining referential integrity.
\n

\n
• Other opinions such as C. Format-preserving encryption are also considered, but the discussion points out that deterministic encryption is a broader category that includes format-preserving encryption.
\n
• Some comments mentioned D. Cryptographic hashing, but it's less suitable because the context mentions reversible encryption is needed.
\n

\nBased on the question and discussion, the AI recommends to agree with the suggested answer A: Deterministic encryption.
\n
\nReasoning:
\nThe question requires de-identification and tokenization while maintaining referential integrity. Deterministic encryption consistently produces the same encrypted output for the same input. This property is crucial for maintaining referential integrity across the database because the tokens generated for the same value will always be the same, allowing joins and relationships to be preserved.
\n
\nWhy other options are not suitable:
\n

\n
• B. Secure, key-based hashes: While secure, hashes are one-way functions and cannot be reversed to recover the original data. This prevents the data from being re-identified if needed. Also, hashes generally are not referentially transparent.
\n
• C. Format-preserving encryption: Format-preserving encryption (FPE) is a type of deterministic encryption that ensures the ciphertext has the same format as the plaintext. While FPE can be suitable, deterministic encryption is a broader term and correctly addresses the need for consistent tokenization to maintain referential integrity.
\n
• D. Cryptographic hashing: Similar to option B, cryptographic hashing is a one-way function, which means the original value cannot be recovered from the hash. The context of the question mentions reversible encryption is needed.
\n

\n
• Cloud Data Loss Prevention, https://cloud.google.com/dlp/docs
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAn office manager at your small startup company is responsible for matching payments to invoices and creating billing alerts. For compliance reasons, the office manager is only permitted to have the Identity and Access Management (IAM) permissions necessary for these tasks. Which two IAM roles should the office manager have? (Choose two.)
\n

From the internet discussion within a period from Q2 2022 to Q1 2025, the conclusion of the answer to this question is C and D, which the reason is that Billing Account Viewer is responsible for viewing cost information and matching payments to invoices, and Billing Account Costs Manager is responsible for creating billing alerts and managing budgets. The supporting documents provided further clarified the permissions required for each role. Some comments pointed out that option E (Billing Account User) would be involved in linking a project to a billing account which is not the scenario described in the question.

The AI agrees with the suggested answer of C and D.
\nReasoning:
\nThe question specifies that the office manager needs to match payments to invoices and create billing alerts. To fulfill these tasks with the least privilege, the appropriate IAM roles are:
\n

\n
• Billing Account Viewer: This role allows the office manager to view billing information, including invoices and payment history, which is necessary for matching payments to invoices.
\n
• Billing Account Costs Manager: This role enables the office manager to create billing alerts and manage budgets. This is essential for creating alerts related to billing activities.
\n

\n
• A. Organization Administrator: This role grants extensive permissions at the organization level, far exceeding what is needed for the specified tasks. It violates the principle of least privilege.
\n
• B. Project Creator: This role is for creating projects, which is not relevant to matching payments or creating billing alerts.
\n
• E. Billing Account User: This role allows a user to link projects to a billing account. While useful in some billing contexts, it's not required for the specific tasks of matching payments to invoices and creating billing alerts.
\n

\nSupporting documentation from Google Cloud IAM roles:
\n

\n
• Billing Account Viewer: Lets you view cost information but not make changes.
\n
• Billing Account Costs Manager: Lets you manage your budgets and receive alerts.
\n

\nConclusion:
\nThe roles Billing Account Viewer and Billing Account Costs Manager provide the necessary permissions for the office manager to perform the required tasks without granting excessive privileges.\n

\n
• Google Cloud Billing Account Roles, https://cloud.google.com/billing/docs/how-to/billing-access
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are designing a new governance model for your organization's secrets that are stored in Secret Manager. Currently, secrets for Production and Non-
Production applications are stored and accessed using service accounts. Your proposed solution must:
✑ Provide granular access to secrets
✑ Give you control over the rotation schedules for the encryption keys that wrap your secrets
✑ Maintain environment separation
✑ Provide ease of management
Which approach should you take?
\n

Agree with Suggested Answer A From the internet discussion within the period from Q2 2022 to Q1 2025, the conclusion of the answer to this question is A, which the reason is to provide granular access to secrets by enforcing access control at the project level using IAM bindings, provide control over the rotation schedules for the encryption keys by using customer-managed encryption keys, and maintain environment separation by using separate Google Cloud projects.\n Some comments consider that C can also be correct as it provides granular access control at the secret level and still provide control over rotation.

\nThe AI agrees with the suggested answer A.
\nReasoning: The question requires granular access, control over rotation schedules for encryption keys, environment separation, and ease of management. Option A fulfills all these requirements:\n

\n
• Separate projects for Production and Non-Production secrets maintain environment separation.
\n
• Project-level IAM bindings provide a good balance between granular access control and ease of management. Granting access at the project level simplifies management compared to individual secret-level permissions.
\n
• Customer-managed encryption keys (CMEK) give you control over the rotation schedules, addressing a key requirement.
\n

\n
• Option B: Using a single project violates the environment separation requirement. Google-managed keys do not provide control over rotation schedules.
\n
• Option C: Using Google-managed keys does not provide control over rotation schedules.
\n
• Option D: Using a single project violates the environment separation requirement.
\n

\n
• Secret Manager Overview, https://cloud.google.com/secret-manager/docs/overview
\n
• Customer-managed encryption keys (CMEK), https://cloud.google.com/kms/docs/cmek
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are a security engineer at a finance company. Your organization plans to store data on Google Cloud, but your leadership team is worried about the security of their highly sensitive data. Specifically, your company is concerned about internal Google employees' ability to access your company's data on Google Cloud.
What solution should you propose?
\n

From the internet discussion, including from Q2 2021 to Q1 2025, the consensus answer to this question is D. Enable Access Transparency logs with Access Approval requests for Google employees. The reason is that this solution provides an additional layer of control and visibility by allowing monitoring and auditing of actions taken by Google personnel when accessing your content, providing an audit trail and the ability to approve or dismiss access requests. Other answers such as using IAM privileges (B) and CMEK are mentioned, however, comments agree that the easiest way is to use Transparency Logs and Access Approval.

The AI suggests that the best answer is indeed D. Enable Access Transparency logs with Access Approval requests for Google employees.

\nReasoning: The question explicitly asks for a solution to address concerns about internal Google employees' access to the company's data. Access Transparency and Access Approval directly address this concern.
\n

\n
• Access Transparency logs provide a near real-time audit trail of Google Cloud personnel actions on customer content. This allows the finance company to monitor and audit access to their data by Google employees.
\n
• Access Approval requires Google to obtain explicit approval before accessing customer data. This gives the finance company control over who at Google can access their data and for what reason.
\n

\n
• A. Use customer-managed encryption keys: While CMEK is a good security practice for data at rest, it doesn't directly address the concern of Google employees accessing the data. CMEK controls who can decrypt the data, but doesn't prevent authorized (but potentially unwanted) access.
\n
• B. Use Google's Identity and Access Management (IAM) service to manage access controls on Google Cloud: IAM is crucial for managing access within the *customer's* organization. It does not apply to Google employees' access to the customer's data.
\n
• C. Enable Admin activity logs to monitor access to resources: Admin Activity logs are useful for monitoring actions taken by users within the customer's Google Cloud project. They don't cover actions taken by Google personnel.
\n

\n
• Access Approval, https://cloud.google.com/security/compliance/access-approval
\n
• Access Transparency, https://cloud.google.com/access-transparency
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou want to use the gcloud command-line tool to authenticate using a third-party single sign-on (SSO) SAML identity provider. Which options are necessary to ensure that authentication is supported by the third-party identity provider (IdP)? (Choose two.)
\n

\nAgree with Suggested Answer. From the internet discussion spanning from Q2 2021 to Q1 2025, the consensus answer to this question is AE, which the reason is the need for SSO SAML as a third-party IdP to ensure the authentication mechanism is SAML for third-party IdP integration and Cloud Identity which provides the infrastructure for identity management with third-party SAML IdPs, enabling SSO authentication. Some users suggested AC where OpenID Connect is considered necessary. However, the majority of comments suggest that OpenID Connect is an option but not a hard requirement. Additionally, options like Identity Platform, Identity-Aware Proxy, or others protocols are not directly related to the SSO SAML authentication with a third-party IdP, therefore, they are not the correct options.\n

The AI agrees with the suggested answer AE.
\nThe question asks about using the gcloud command-line tool to authenticate with a third-party SSO SAML identity provider. Two options are necessary: SSO SAML as a third-party IdP (Option A) and Cloud Identity (Option E).
\n
\nReasoning:
\n

\n
• Option A (SSO SAML as a third-party IdP): This is crucial because the question explicitly mentions using a third-party SSO SAML identity provider. gcloud needs to be configured to use SAML for authentication against this provider.
\n
• Option E (Cloud Identity): Cloud Identity is Google's Identity as a Service (IDaaS) offering, which enables you to manage users and groups, and importantly, configure SSO with third-party SAML providers. It acts as the bridge between your Google Cloud environment and your existing identity provider.
\n

\n
• Option B (Identity Platform): Identity Platform provides customer identity and access management (CIAM) and focuses more on application-level authentication and authorization for end-users, rather than authenticating the gcloud CLI itself against a third-party IdP.
\n
• Option C (OpenID Connect): While OpenID Connect is an authentication protocol, the question specifically mentions SAML. OpenID Connect might be used *in addition* to SAML in some scenarios, but it's not necessary to ensure that authentication is supported by a third-party *SAML* IdP, and not directly related to authenticating gcloud CLI.
\n
• Option D (Identity-Aware Proxy): Identity-Aware Proxy (IAP) controls access to cloud applications running on Google Cloud Platform. It's not directly involved in authenticating the gcloud CLI to the Google Cloud environment itself.
\n

In summary, selecting SSO SAML as a third-party IdP and Cloud Identity enables the gcloud command-line tool to authenticate using a third-party SAML IdP.

\nCitations:\n

\n
• Cloud Identity, https://cloud.google.com/identity
\n
• gcloud auth login, https://cloud.google.com/sdk/gcloud/reference/auth/login
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou work for a large organization where each business unit has thousands of users. You need to delegate management of access control permissions to each business unit. You have the following requirements:
✑ Each business unit manages access controls for their own projects.
✑ Each business unit manages access control permissions at scale.
✑ Business units cannot access other business units' projects.
✑ Users lose their access if they move to a different business unit or leave the company.
✑ Users and access control permissions are managed by the on-premises directory service.
What should you do? (Choose two.)
\n

From the internet discussion, spanning from Q2 2023 to Q1 2025, the consensus is that the correct answers are B and E. The reasoning is that option B, creating separate folders for each organizational unit (OU) and respective projects under those folders, promotes OU independence in terms of environments, and option E, utilizing Google Cloud Directory Sync (GCDS), aligns with the requirement to manage users from an on-premise directory. This approach ensures isolation between OUs and facilitates centralized user management.

\nBased on the question's requirements and the discussion, the AI agrees with the suggested answer of B and E.
\n
\nReasoning:
\n

\n
• Option B: Organize projects in folders, and assign permissions to Google groups at the folder level. This approach aligns with the requirement that each business unit manages access controls for its own projects and at scale. Folders provide a hierarchical structure for organizing projects, and assigning permissions to Google Groups at the folder level simplifies access management for a large number of users. This also addresses the requirement that business units cannot access each other's projects by granting permissions only within their respective folders.
\n
• Option E: Use Google Cloud Directory Sync to synchronize users and group memberships in Cloud Identity. This directly addresses the requirement that users and access control permissions are managed by the on-premises directory service. Google Cloud Directory Sync (GCDS) synchronizes users and group memberships from the on-premises directory to Cloud Identity, ensuring that user accounts and group memberships are consistent across both environments. When users move between business units or leave the company, these changes are reflected in the on-premises directory and synchronized to Cloud Identity, automatically updating their access permissions.
\n

\n
• Option A: Use VPC Service Controls to create perimeters around each business unit's project. VPC Service Controls primarily focuses on data exfiltration prevention at the network level. While it can add a layer of security, it doesn't directly address the requirement of delegating access control management to each business unit. It mainly controls access to Google Cloud services based on the network origin of the request, which is not the primary concern here.
\n
• Option C: Group business units based on Organization Units (OUs) and manage permissions based on OUs. While using OUs for organizing resources is a valid strategy, the question states that the users and permissions are managed by the on-premises directory service. Simply grouping business units based on OUs in Google Cloud does not directly integrate with or leverage the on-premises directory for managing access.
\n
• Option D: Create a project naming convention, and use Google's IAM Conditions to manage access based on the prefix of project names. While a naming convention is a good practice, relying solely on IAM Conditions based on project name prefixes for access control is not a scalable or robust solution. It can become difficult to manage and maintain as the number of projects and business units grows. It also doesn't directly address the integration with the on-premises directory service.
\n

\n
• Google Cloud Directory Sync, https://support.google.com/cloudidentity/answer/1060565?hl=en
\n
• IAM Overview, https://cloud.google.com/iam/docs/overview
\n
• Folders overview, https://cloud.google.com/resource-manager/docs/creating-managing-folders
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour organization recently deployed a new application on Google Kubernetes Engine. You need to deploy a solution to protect the application. The solution has the following requirements:
✑ Scans must run at least once per week
✑ Must be able to detect cross-site scripting vulnerabilities
✑ Must be able to authenticate using Google accounts
Which solution should you use?
\n

Agree with Suggested Answer. From the internet discussion, which includes posts from Q2 2022 to Q2 2024, the conclusion of the answer to this question is B. Web Security Scanner, which the reason is the Web Security Scanner identifies security vulnerabilities in web applications running in App Engine, Google Kubernetes Engine (GKE), and Compute Engine. Although some comments suggest that Google Cloud Armor (A) could also prevent XSS attacks, the majority of the discussion supports Web Security Scanner as the correct answer.

The AI agrees with the suggested answer, which is B. Web Security Scanner.
\n
\nReasoning:
\nWeb Security Scanner is designed to identify vulnerabilities in web applications, including cross-site scripting (XSS), and it can be configured to run on a weekly schedule. The key requirements in the question are detecting XSS vulnerabilities and running scans at least once a week, which Web Security Scanner fulfills.\n
\n
\nFurthermore, the Web Security Scanner is suited for applications running on Google Kubernetes Engine (GKE), App Engine, and Compute Engine, as specified in the documentation.\n
\n
\nWhy other options are not suitable:
\n

\n
• A. Google Cloud Armor: While Cloud Armor can protect against XSS by filtering malicious requests, it's primarily a Web Application Firewall (WAF) and doesn't actively scan for vulnerabilities in the application code. It prevents attacks but doesn't detect vulnerabilities proactively.
\n
• C. Security Health Analytics: Security Health Analytics is part of Security Command Center and focuses on identifying misconfigurations and compliance violations in your Google Cloud environment, not web application vulnerabilities like XSS.
\n
• D. Container Threat Detection: This service focuses on detecting threats within containerized environments at runtime, such as unexpected process executions or file system modifications. It doesn't scan for web application vulnerabilities.
\n

\n
• Web Security Scanner Overview, https://cloud.google.com/web-security-scanner/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tAn organization is moving applications to Google Cloud while maintaining a few mission-critical applications on-premises. The organization must transfer the data at a bandwidth of at least 50 Gbps. What should they use to ensure secure continued connectivity between sites?
\n

From the internet discussion from Q2 2022 to Q1 2025, the conclusion of the answer to this question is A. Dedicated Interconnect, which the reason is because Dedicated Interconnect can guarantee throughput of at least 50 Gbps. Comments highlighted that Partner Interconnect may not always support 50 Gbps, the maximum supported attachment size is 50 Gbps. Therefore, Dedicated Interconnect is the correct answer as it meets the requirement of \"at least\" 50 Gbps.\n

The AI agrees with the suggested answer, which is A. Dedicated Interconnect.
\n
\nReasoning:
\nThe question specifies a bandwidth requirement of at least 50 Gbps for secure and continuous connectivity between on-premises and Google Cloud environments. Dedicated Interconnect offers the necessary bandwidth and dedicated connection required to meet this need.
\n

\n
• Dedicated Interconnect: Provides a direct physical connection to Google's network, guaranteeing high bandwidth (up to 100 Gbps) and low latency, making it suitable for mission-critical applications and large data transfers.
\n

\n
• Cloud Router: Cloud Router is a distributed and fully managed Google Cloud service that dynamically exchanges routes between your Virtual Private Cloud (VPC) network and your on-premises network using BGP. While essential for hybrid connectivity, it doesn't provide the physical connection or guaranteed bandwidth. It works in conjunction with Cloud VPN or Interconnect.
\n
• Cloud VPN: Cloud VPN provides encrypted VPN tunnels through the public internet. While secure, it does not guarantee a bandwidth of 50 Gbps and is subject to internet traffic conditions, making it unsuitable for high-bandwidth, mission-critical applications. The bandwidth is also limited compared to Dedicated Interconnect.
\n
• Partner Interconnect: Partner Interconnect provides connectivity to Google Cloud through a supported service provider. While it can offer high bandwidth, it does not always guarantee a minimum of 50 Gbps. The maximum supported attachment size can be 50 Gbps, but this doesn't mean that the connection will reliably provide at least 50 Gbps. The bandwidth depends on the partner's capabilities and the specific service offering.
\n

Therefore, Dedicated Interconnect is the most suitable option for fulfilling the specified requirements.

In Summary:
\nSuggested Answer: A. Dedicated Interconnect
\nReason: It guarantees the throughput of at least 50 Gbps.
\nReasons for not choosing other answers: Partner Interconnect may not always support 50 Gbps. Cloud VPN does not guarantee such high bandwidth and uses the public internet. Cloud Router is a service used in conjunction with VPN or Interconnect, but doesn't provide the bandwidth itself.

\n
• Citations:
\n
• Google Cloud Interconnect, https://cloud.google.com/network-connectivity/docs/interconnect/concepts/dedicated-interconnect
\n
• Google Cloud Partner Interconnect, https://cloud.google.com/network-connectivity/docs/interconnect/concepts/partner-interconnect
\n
• Google Cloud VPN, https://cloud.google.com/vpn/docs/concepts/overview
\n
• Google Cloud Router, https://cloud.google.com/router/docs/concepts/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour organization has had a few recent DDoS attacks. You need to authenticate responses to domain name lookups. Which Google Cloud service should you use?
\n

Agreed with Suggested Answer. From the internet discussion within the period (from Q2 2023 to Q4 2024), the consensus answer to this question is A. Cloud DNS with DNSSEC, which the reason is DNSSEC authenticates responses to domain name lookups, protecting against spoofing and poisoning attacks.
\n

\n
• The comments agree that DNSSEC is the correct choice because it is designed to secure DNS by authenticating responses and preventing attackers from manipulating DNS requests.
\n
• The key here is the protection of domain name lookups, and only DNSSEC can do that.
\n

\nBased on the question and discussion, the AI recommends answer A: Cloud DNS with DNSSEC.
\n
\nReasoning:
\nThe question specifically asks for a solution to authenticate responses to domain name lookups, which directly relates to the core functionality of DNSSEC. DNSSEC (Domain Name System Security Extensions) is a suite of security extensions to the DNS protocol that provides origin authentication of DNS data, authenticated denial of existence, and data integrity. It uses cryptographic signatures to ensure that DNS data has not been tampered with during transit and that it comes from the authoritative source.
\n
\nWhy other options are incorrect:
\n

\n
• B. Cloud NAT: Cloud NAT (Network Address Translation) is used to allow instances without external IP addresses to create outbound connections to the internet. It does not provide any security features related to DNS or authentication of DNS responses.
\n
• C. HTTP(S) Load Balancing: HTTP(S) Load Balancing distributes incoming HTTP(S) traffic across multiple backend instances. While it can provide some protection against DDoS attacks by distributing the load, it doesn't authenticate DNS responses.
\n
• D. Google Cloud Armor: Google Cloud Armor provides protection against DDoS attacks and web application attacks at the network edge. While it can mitigate some effects of DDoS attacks that target DNS servers, it does not authenticate the DNS responses themselves. It focuses more on filtering malicious traffic before it reaches the application or infrastructure.
\n

\nCitations:\n

\n
• Cloud DNS, https://cloud.google.com/dns/docs
\n
• DNSSEC Overview, https://www.cloudflare.com/learning/dns/dnssec/what-is-dnssec/
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour Security team believes that a former employee of your company gained unauthorized access to Google Cloud resources some time in the past 2 months by using a service account key. You need to confirm the unauthorized access and determine the user activity. What should you do?
\n

Agree with Suggested Answer From the internet discussion from Q2 2023 to Q3 2024, the conclusion of the answer to this question is D, which the reason is that to check user activity, you need to use audit logs and analyze them in Cloud Logging (Log Explorer). This will help to investigate unauthorized access. Other options are not related or provide only metrics instead of logs and can be used for monitoring, not for checking user activity.

The AI agrees with the suggested answer, which is D: Use the Logs Explorer to search for user activity.

\nReasoning:
\nThe primary goal is to confirm unauthorized access and determine user activity related to a potentially compromised service account key. Logs Explorer (part of Cloud Logging) is the appropriate tool for this task because it allows you to search and analyze audit logs. These logs contain detailed information about user actions within Google Cloud, including which service account key was used and what resources were accessed. This enables the security team to confirm the unauthorized access and trace the former employee's activities.

\nHere's why the other options are not suitable:
\n* A: Use Security Health Analytics to determine user activity. Security Health Analytics is for identifying security misconfigurations and vulnerabilities. While it can provide insights into potential security risks, it doesn't directly show user activity logs needed to investigate unauthorized access incidents.\n* B: Use the Cloud Monitoring console to filter audit logs by user. Cloud Monitoring primarily focuses on performance metrics and uptime monitoring, not on detailed analysis of audit logs for security investigations. While you can view some basic log data in Monitoring, it lacks the advanced search and filtering capabilities of Logs Explorer.\n* C: Use the Cloud Data Loss Prevention API to query logs in Cloud Storage. Cloud DLP is designed to discover, classify, and protect sensitive data. It's not the right tool for investigating general user activity or unauthorized access events recorded in audit logs. It's focused on data exfiltration risks and content inspection, not user behavior analysis.

\nTherefore, Logs Explorer is the most direct and effective tool for the specified security investigation.\n

\n
• Citations:
\n
• Cloud Logging, https://cloud.google.com/logging
\n
• Cloud Monitoring, https://cloud.google.com/monitoring
\n
• Security Health Analytics, https://cloud.google.com/security-command-center/docs/how-to-use-security-health-analytics
\n
• Cloud Data Loss Prevention (DLP), https://cloud.google.com/dlp
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company requires the security and network engineering teams to identify all network anomalies within and across VPCs, internal traffic from VMs to VMs, traffic between end locations on the internet and VMs, and traffic between VMs to Google Cloud services in production. Which method should you use?
\n

From the internet discussion including from Q2 2021 to Q1 2025, the conclusion of the answer to this question is B. Configure packet mirroring policies, which the reason is that packet mirroring allows for comprehensive inspection of all traffic, crucial for detecting anomalies and threats. The question asks for anomaly detection, and packet mirroring exports all traffic, making it suitable for detecting suspicious flows, and it is necessary for identifying all network anomalies within and across VPCs.\n

\nOther opinions:\n

\n
• Some users suggested C as a viable solution. The reason being, VPC flow logs can be used for network monitoring and analysis. However, C is limited to the subnet level.
\n

The AI agrees with the suggested answer of B. Configure packet mirroring policies.
\nReasoning: Packet mirroring provides a comprehensive solution for capturing and inspecting network traffic, which is essential for identifying anomalies across various traffic types as outlined in the question. It allows capturing traffic within and across VPCs, internal VM-to-VM traffic, traffic between internet endpoints and VMs, and traffic between VMs and Google Cloud services. Packet mirroring provides a deep level of inspection needed to detect anomalies by duplicating traffic for analysis without impacting the production network. This meets the core requirement of the question, which is to identify *all* network anomalies across all specified traffic types.
\nWhy other options are not suitable:\n

\n
• A. Define an organization policy constraint: Organization policy constraints are primarily used for enforcing configuration standards and restrictions across an organization. They do not provide real-time traffic inspection or anomaly detection capabilities.
\n
• C. Enable VPC Flow Logs on the subnet: VPC Flow Logs capture metadata about network flows, such as source and destination IP addresses, ports, and the number of bytes transferred. While useful for network monitoring and auditing, Flow Logs do not capture the actual packet data, which is necessary for in-depth anomaly detection that requires inspection of packet content. Additionally, VPC Flow Logs are sampled, potentially missing sporadic anomalous events.
\n
• D. Monitor and analyze Cloud Audit Logs: Cloud Audit Logs record administrative actions and access to Google Cloud resources. While useful for security auditing and compliance, they do not provide network traffic inspection or anomaly detection capabilities.
\n

\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company has been creating users manually in Cloud Identity to provide access to Google Cloud resources. Due to continued growth of the environment, you want to authorize the Google Cloud Directory Sync (GCDS) instance and integrate it with your on-premises LDAP server to onboard hundreds of users. You are required to:
✑ Replicate user and group lifecycle changes from the on-premises LDAP server in Cloud Identity.
✑ Disable any manually created users in Cloud Identity.
You have already configured the LDAP search attributes to include the users and security groups in scope for Google Cloud. What should you do next to complete this solution?
\n

Agree with Suggested Answer From the internet discussion from Q2 2023 to Q4 2024, the conclusion of the answer to this question is A, which the reason is configuring GCDS to suspend rather than delete accounts if user accounts are not found in the LDAP directory in GCDS. The comments also mentioned that the reference is from Google official document: https://support.google.com/a/answer/7177267.

\n The AI agrees with the suggested answer (A).
\n Here's a detailed explanation:\n

\n The primary requirement is to replicate user and group lifecycle changes from the on-premises LDAP server to Cloud Identity and disable manually created users in Cloud Identity.\n

\n Here's why option A is the best approach:\n

\n
• \n1. Configure the option to suspend domain users not found in LDAP:\n This step addresses the requirement to disable manually created users. When GCDS syncs, it will compare the users in the LDAP directory with those in Cloud Identity. Any users that exist in Cloud Identity but are not found in the LDAP directory (which would include the manually created users) will be suspended. This effectively disables them without deleting them, which aligns with a safe and reversible approach.\n
\n
• \n2. Set up a recurring GCDS task:\n This ensures that the synchronization between the on-premises LDAP server and Cloud Identity happens automatically on a regular schedule. This way, any changes made to users and groups in the LDAP server (e.g., new users, deleted users, group membership changes) are automatically reflected in Cloud Identity. This satisfies the requirement to replicate lifecycle changes.\n
\n

\n Here's why the other options are not as suitable:\n

\n
• \nOption B: Deleting users not found in LDAP might be too aggressive. It's generally better to suspend accounts first, as deletion is irreversible. If a user is accidentally removed from the LDAP directory, deleting their account in Cloud Identity would cause data loss and disruption.\n
\n
• \nOptions C and D: Excluding manually created users from the LDAP search attributes would prevent GCDS from managing these users. The goal is to *disable* manually created users, not ignore them completely. Excluding them wouldn't disable them. Also, running GCDS only after user/group changes (Options B and D) is less ideal than a recurring task. A recurring task ensures consistent synchronization, even if changes are missed.\n
\n

\n In summary, option A provides the most appropriate and safest method to meet the requirements of replicating user lifecycle changes and disabling manually created users.\n

\nTherefore, based on the problem description and discussion, option A is the suggested answer because it suspends accounts instead of deleting them and sets up a recurring task.\n

\n Citations:\n

\n
• Google Cloud Directory Sync Help, https://support.google.com/a/answer/7177267
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are troubleshooting access denied errors between Compute Engine instances connected to a Shared VPC and BigQuery datasets. The datasets reside in a project protected by a VPC Service Controls perimeter. What should you do?
\n

From the internet discussion, including from Q2 2021 to Q1 2025, the conclusion of the answer to this question is A. Add the host project containing the Shared VPC to the service perimeter., which the reason is if Shared VPC is used, it's necessary to include the host project in the service perimeter along with any service projects that belong to the Shared VPC. Several comments have referenced this requirement directly. Other opinions included B or C, but they are considered less correct since the setup is already a Shared VPC and there is no need to create a new perimeter or include the service project only.\n

The AI agrees with the suggested answer A.
\nThe correct answer is A: Add the host project containing the Shared VPC to the service perimeter.
\nReasoning: When using Shared VPC, the host project acts as the central point of control for networking resources. To allow Compute Engine instances in service projects to access BigQuery datasets protected by a VPC Service Controls perimeter, it's crucial to include the host project within the same service perimeter. This ensures that all traffic originating from the Shared VPC network is subject to the perimeter's rules and restrictions. Without including the host project, the access to BigQuery datasets will be denied, as the requests will appear to originate from outside the permitted perimeter.
\nReasons for excluding other options:
\n

\n
• B: Adding only the service project to the perimeter would not solve the problem because the network traffic originates from the Shared VPC, which is managed at the host project level.
\n
• C: Creating a new service perimeter between the service and host projects is not the correct approach. The goal is to allow access to an existing protected resource (BigQuery dataset), so the existing perimeter needs to be modified or bridged.
\n
• D: Creating a perimeter bridge between the service project and the perimeter containing the BigQuery datasets might seem like a viable option, but it's generally used for more complex scenarios where you need to allow limited, controlled access between completely separate perimeters. In this case, the simpler and more direct solution is to include the host project in the existing perimeter.
\n

Citations:

\n
• VPC Service Controls, https://cloud.google.com/vpc-service-controls
\n
• Shared VPC, https://cloud.google.com/vpc/docs/shared-vpc
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou recently joined the networking team supporting your company's Google Cloud implementation. You are tasked with familiarizing yourself with the firewall rules configuration and providing recommendations based on your networking and Google Cloud experience. What product should you recommend to detect firewall rules that are overlapped by attributes from other firewall rules with higher or equal priority?
\n

From the internet discussion from Q2 2023 to Q1 2025, the conclusion of the answer to this question is D. Firewall Insights, which the reason is that Firewall Insights analyzes firewall rules to detect rules that are shadowed by other rules. Several users agreed with this answer, providing the documentation from Google Cloud as a reference.\n

\nBased on the question and discussion, the AI recommends answer D: Firewall Insights.
\n
\nReasoning:
\nThe question asks for a product that can detect overlapped firewall rules. Firewall Insights is specifically designed for this purpose. It analyzes firewall configurations to identify overly permissive, shadowed, or otherwise problematic rules.
\n
\nFirewall Insights helps in identifying the following:
\n

\n
• Shadowed rules: Rules that are ineffective because other, higher-priority rules cover the same traffic.
\n
• Redundant rules: Rules that duplicate the function of other rules.
\n
• Overly permissive rules: Rules that allow more traffic than intended.
\n

\n
• A. Security Command Center: Security Command Center provides a broader security overview and threat detection capabilities. While it integrates with various Google Cloud services, it isn't primarily focused on detailed firewall rule analysis and overlap detection.
\n
• B. Firewall Rules Logging: Firewall Rules Logging captures information about traffic that matches firewall rules. Analyzing these logs can help understand traffic patterns and rule effectiveness, but it doesn't automatically detect overlapping rules. It would require manual analysis or custom scripting.
\n
• C. VPC Flow Logs: VPC Flow Logs record network traffic sent from and received by VM instances. Similar to Firewall Rules Logging, it requires additional analysis to detect firewall rule overlaps and is not its primary function.
\n

\nIn summary, Firewall Insights is the most suitable tool for the task of detecting overlapped firewall rules due to its specific functionality for analyzing and identifying inefficiencies in firewall configurations.\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tThe security operations team needs access to the security-related logs for all projects in their organization. They have the following requirements:
✑ Follow the least privilege model by having only view access to logs.
✑ Have access to Admin Activity logs.
✑ Have access to Data Access logs.
✑ Have access to Access Transparency logs.
Which Identity and Access Management (IAM) role should the security operations team be granted?
\n

From the internet discussion within the period from Q2 2021 to Q1 2025, the consensus of the answer to this question is A. roles/logging.privateLogViewer, which the reason is the security operations team needs to have access to specific logs across all projects, including Admin Activity logs, Data Access logs, and Access Transparency logs. roles/logging.privateLogViewer includes all the permissions contained by roles/logging.viewer, plus the ability to read Data Access audit logs. Other opinions suggest other roles; however, they are not the correct answer as they do not provide the necessary access to all required logs.

The AI agrees with the suggested answer of A (roles/logging.privateLogViewer).\n
\n
\nReasoning: The question specifies that the security operations team needs view access to Admin Activity logs, Data Access logs, and Access Transparency logs across all projects while adhering to the principle of least privilege.\n
\n

\n
• roles/logging.privateLogViewer: This role grants the ability to read all log data, including Admin Activity, Data Access, and Access Transparency logs, which aligns with the requirements.
\n

\n
• roles/logging.admin: This role provides administrative access to logging configurations, which is more than what the security operations team needs (view access only). This violates the least privilege principle.
\n
• roles/viewer: This role provides very broad read-only access to many Google Cloud resources but does not guarantee access to the specific logs required (Admin Activity, Data Access, Access Transparency). It's too broad and might not include the necessary permissions for all log types.
\n
• roles/logging.viewer: This role allows viewing of logs, but it specifically excludes Data Access logs. The question explicitly requires access to Data Access logs, making this role unsuitable.
\n

\n
• Cloud Logging roles, https://cloud.google.com/logging/docs/access-control
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are exporting application logs to Cloud Storage. You encounter an error message that the log sinks don't support uniform bucket-level access policies. How should you resolve this error?
\n

Agree with Suggested Answer. From the internet discussion including from Q2 2023 to Q4 2024, the conclusion of the answer to this question is A. Change the access control model for the bucket, which the reason is that the error message indicates log sinks don't support uniform bucket-level access policies, and this issue can be resolved by changing the access control model. This allows you to enable uniform bucket-level access, which is required for log sinks to function properly. The comments provide citations to Google Cloud documentation to support this. Additionally, the comments highlight that if bucket-level access policies are not supported, fine-grained access is being used and that the recommended architecture is uniform bucket-level access. Another opinion suggests that option B is not fully correct because it would require another bucket without uniform bucket-level access.

Based on the question and discussion, the AI agrees with the suggested answer A: Change the access control model for the bucket.
\n
\nReasoning:
\nThe error message explicitly states that the log sinks do not support uniform bucket-level access policies. This implies that the bucket is currently configured with fine-grained access control (ACLs). To resolve this, you need to switch the bucket to use uniform bucket-level access. This ensures that IAM policies are the sole method for controlling access to the bucket, which is a requirement for log sinks to function correctly in this scenario. The discussion and provided links confirm that this is the correct approach.\n
\n
\nWhy other options are incorrect:\n

\n
• B. Update your sink with the correct bucket destination: While ensuring the sink points to the correct bucket is important, it doesn't address the underlying issue of incompatible access control policies. Changing the bucket destination wouldn't solve the problem if the new bucket also doesn't support the log sink requirements.
\n
• C. Add the roles/logging.logWriter Identity and Access Management (IAM) role to the bucket for the log sink identity: Adding the `roles/logging.logWriter` IAM role is essential for granting the log sink permission to write logs to the bucket. However, this action alone will not resolve the incompatibility with uniform bucket-level access. The bucket must first be configured to use uniform bucket-level access.
\n
• D. Add the roles/logging.bucketWriter Identity and Access Management (IAM) role to the bucket for the log sink identity: Similar to option C, adding `roles/logging.bucketWriter` grants necessary permissions but does not address the core issue of the bucket's access control model. This role might even be less appropriate than `roles/logging.logWriter` since it grants broader access than just writing logs.
\n

In summary, changing the access control model to uniform bucket-level access is the necessary step to resolve the error and allow the log sink to function correctly.

\n
• Cloud Logging, https://cloud.google.com/logging
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou plan to deploy your cloud infrastructure using a CI/CD cluster hosted on Compute Engine. You want to minimize the risk of its credentials being stolen by a third party. What should you do?
\n

Agree with Suggested Answer From the internet discussion within period (e.g. from Q2 2021 to Q1 2025), the conclusion of the answer to this question is C. Create a custom service account for the cluster and enable the constraints/iam.disableServiceAccountKeyCreation organization policy at the project level, which the reason is to minimize the risk of credentials being stolen by a third party, you should control the use of unmanaged long-term credentials. The consensus is that this is achieved by creating a custom service account to control the permissions and access, and then disabling the creation of service account keys using the organization policy constraints/iam.disableServiceAccountKeyCreation, which prevents the unauthorized access to the service account’s credentials and reduces the risk of the service account keys being exported outside GCP to local machines. References were made to the official Google Cloud documentation.

The AI agrees with the suggested answer, which is C. Create a custom service account for the cluster. Enable the constraints/iam.disableServiceAccountKeyCreation organization policy at the project level.
\n
\nReasoning:
\nTo minimize the risk of credentials being stolen, it's crucial to control the creation and usage of service account keys. Service accounts are used to grant your CI/CD cluster (running on Compute Engine) permissions to access other Google Cloud resources. If a service account key is compromised, it can be used by a malicious actor to impersonate your cluster and access those resources. Disabling service account key creation mitigates this risk.
\n

\n
• Creating a custom service account allows you to grant the cluster only the necessary permissions, following the principle of least privilege.
\n
• The constraints/iam.disableServiceAccountKeyCreation organization policy prevents users from creating new service account keys, reducing the attack surface.
\n

\n
• A: Using a Cloud Identity user account for the cluster is not the best practice. Service accounts are better suited for applications and automated processes like CI/CD. Storing user credentials in a vault adds complexity and introduces another potential point of failure.
\n
• B: Disabling service account creation (constraints/iam.disableServiceAccountCreation) would prevent the creation of *any* service accounts, including the one needed for the cluster. This would halt the CI/CD pipeline.
\n
• D: The constraints/iam.allowServiceAccountCredentialLifetimeExtension organization policy allows the extension of service account credential lifetimes. Allowing lifetime extension increases the risk of a compromised key being used for a longer duration, hence is against security best practices.
\n

\n
• Google Cloud Organization Policy Constraints, https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints
\n
• Google Cloud Service Accounts, https://cloud.google.com/iam/docs/service-accounts
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to set up two network segments: one with an untrusted subnet and the other with a trusted subnet. You want to configure a virtual appliance such as a next-generation firewall (NGFW) to inspect all traffic between the two network segments. How should you design the network to inspect the traffic?
\n

Agree with Suggested Answer. From the internet discussion from Q2 2021 to Q1 2025, the conclusion of the answer to this question is D. Set up two VPC networks: one trusted and the other untrusted. Configure a virtual appliance using multiple network interfaces, with each interface connected to one of the VPC networks, which the reason is this architecture has multiple VPC networks that are bridged by an L7 next-generation firewall (NGFW) appliance, which functions as a multi-NIC bridge between VPC networks. \n

\n
• Some users considered that B is the correct answer.
\n
• One user initially questioned D, but then agreed that is the correct answer because the question is asking to design the network.
\n

Based on the analysis of the question and discussion, the AI assistant agrees with the suggested answer D.
\n
\nReasoning:
\nThe best approach to inspect traffic between trusted and untrusted networks using a virtual appliance (NGFW) is to isolate the networks into separate VPCs and use the NGFW as a bridge between them. This provides clear segmentation and allows the NGFW to inspect all traffic as it passes between the VPCs. Option D achieves this by creating two separate VPC networks and connecting the NGFW with multiple network interfaces, one in each VPC. This forces all traffic between the networks to pass through the appliance.
\n
\nWhy other options are not the best:
\n

\n
• Option A and B: Using a single VPC with two subnets doesn't provide true network segmentation. A compromised host in the \"untrusted\" subnet could potentially access resources in the \"trusted\" subnet directly, bypassing the NGFW, if not properly configured. Furthermore, routing all traffic (0.0.0.0/0) to the virtual appliance (Option A) would break external connectivity and is not the correct approach for inspecting traffic between internal segments. Routing RFC1918 traffic (Option B) may miss other potential traffic.
\n
• Option C: While VPC peering allows connectivity between VPCs, it doesn't inherently force all traffic through the NGFW. Custom routes would need to be very carefully configured to ensure all traffic passes through the appliance, and this could be complex to manage. Option D provides a simpler and more direct way to ensure traffic inspection.
\n

In summary, Option D provides the most secure and manageable solution for inspecting traffic between trusted and untrusted network segments using a virtual appliance.\n

Answer: D

\n
• Next-generation firewall, https://en.wikipedia.org/wiki/Next-generation_firewall
\n
• RFC1918 - Address Allocation for Private Internets, https://datatracker.ietf.org/doc/html/rfc1918
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are a member of your company's security team. You have been asked to reduce your Linux bastion host external attack surface by removing all public IP addresses. Site Reliability Engineers (SREs) require access to the bastion host from public locations so they can access the internal VPC while off-site. How should you enable this access?
\n

From the internet discussion, which spans from approximately Q2 2023 to Q4 2024, the consensus is to agree with the suggested answer C. The reason is that, to enable access to the bastion host from public locations while reducing the Linux bastion host external attack surface by removing all public IP addresses, the implementation of Identity-Aware Proxy TCP forwarding is the correct solution. This allows Site Reliability Engineers (SREs) to access the internal VPC while off-site securely by verifying user identity and context of the request before granting access. This approach provides secure access to TCP-based applications, such as SSH, without exposing them to the internet, as referenced in the comments.

\nThe AI agrees with the suggested answer, which is C. Implement Identity-Aware Proxy TCP forwarding for the bastion host.
\n
\nReasoning:
\nThe primary requirement is to remove public IP addresses from the bastion host to reduce the attack surface while still allowing SREs to access it from public locations. IAP (Identity-Aware Proxy) TCP forwarding precisely addresses this.
\n

\n
• Secure Access: IAP allows you to control access to your applications running on Compute Engine based on the user's identity and context. It verifies the user's identity before granting access, adding an extra layer of security.
\n
• No Public IPs: With IAP TCP forwarding, the bastion host doesn't need a public IP address. SREs connect through the IAP service, which forwards the connection to the bastion host on the internal network. This significantly reduces the attack surface.
\n
• Centralized Access Control: IAP provides a centralized way to manage access to the bastion host, making it easier to enforce security policies.
\n

\n
• A. Implement Cloud VPN for the region where the bastion host lives: While Cloud VPN provides secure connectivity, it's more suitable for site-to-site connections or connecting entire networks. It doesn't directly address the need to remove public IPs from the bastion host. VPNs also introduce additional operational overhead.
\n
• B. Implement OS Login with 2-step verification for the bastion host: OS Login enhances authentication but doesn't eliminate the need for a public IP address on the bastion host, thus not fulfilling the requirement of reducing the external attack surface by removing public IPs.
\n
• D. Implement Google Cloud Armor in front of the bastion host: Google Cloud Armor protects against DDoS and other web-based attacks. While it improves security, it doesn't remove the need for a public IP address on the bastion host. It's designed for web applications, not general TCP forwarding.
\n

\n
• Identity-Aware Proxy, https://cloud.google.com/iap
\n
• Cloud VPN, https://cloud.google.com/vpn/docs
\n
• OS Login, https://cloud.google.com/compute/docs/oslogin/
\n
• Google Cloud Armor, https://cloud.google.com/armor/docs
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to enable VPC Service Controls and allow changes to perimeters in existing environments without preventing access to resources. Which VPC Service
Controls mode should you use?
\n

Agreed with the Suggested Answer. From the internet discussion from Q2 2023 to Q4 2024, the conclusion of the answer to this question is D. Dry run, which the reason is dry run mode allows testing of perimeter configurations and monitoring of service usage without blocking access to resources. Also, the comments pointed out that options A and B are not applicable and C is too restrictive for this use case.

\nThe AI agrees with the suggested answer.
\nSuggested Answer: D. Dry run
\n
\nReasoning:\n

\n
• The question asks for a VPC Service Controls mode that allows changes to perimeters in existing environments without preventing access to resources.
\n
• Dry run mode is specifically designed for this purpose. It allows you to test your perimeter configurations and monitor how they would affect your service usage without actually enforcing the restrictions. This is ideal for understanding the impact of your changes before fully implementing them.
\n

\n
• A. Cloud Run: Cloud Run is a managed compute platform for deploying and scaling containerized applications. It's not a VPC Service Controls mode.
\n
• B. Native: Native likely refers to using VPC Service Controls in its standard or 'native' enforcement mode, which would enforce the perimeter and potentially block access, conflicting with the question's requirement.
\n
• C. Enforced: Enforced mode means VPC Service Controls are actively blocking access that violates the perimeter. This would prevent changes without preventing access.
\n

Therefore, Dry run mode is the most appropriate choice because it allows for testing and modification of perimeters without disrupting existing access to resources.

The reason of choosing this answer is dry run mode allows testing of perimeter configurations and monitoring of service usage without blocking access to resources

The reason of not choosing the other answers: options A and B are not applicable and C is too restrictive for this use case.

\nCitations:\n

\n
• VPC Service Controls Overview, https://cloud.google.com/vpc-service-controls/docs/overview
\n
• VPC Service Controls Dry Run, https://cloud.google.com/vpc-service-controls/docs/dry-run
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou manage your organization's Security Operations Center (SOC). You currently monitor and detect network traffic anomalies in your Google Cloud VPCs based on packet header information. However, you want the capability to explore network flows and their payload to aid investigations. Which Google Cloud product should you use?
\n

Agree with Suggested Answer From the internet discussion, the conclusion of the answer to this question is D. Packet Mirroring, which the reason is Packet Mirroring captures all traffic and packet data, including payloads and headers, allowing for deep packet analysis. Several users cited the official Google Cloud documentation as a reference.\n

\nThe AI assistant agrees with the suggested answer.
\nThe recommended Google Cloud product to explore network flows and their payload for investigations is D. Packet Mirroring.
\nReasoning: Packet Mirroring allows you to copy network traffic from Compute Engine instances and forward it for inspection. This includes both packet headers and the packet payload, which is necessary for deep packet analysis and investigation of network flows. This provides the SOC with the capability to explore the full network traffic, enabling a detailed investigation of anomalies.
\nWhy other options are not suitable:\n

\n
• A. Marketplace IDS: While a Marketplace IDS can inspect traffic, it's an additional cost and complexity, and Packet Mirroring can feed directly into existing security tools.
\n
• B. VPC Flow Logs: VPC Flow Logs only capture metadata about network flows (source, destination, ports, etc.) and do not include the packet payload. They are useful for identifying traffic patterns but not for deep packet inspection.
\n
• C. VPC Service Controls logs: VPC Service Controls are used to establish a security perimeter around Google Cloud resources and control data movement. The logs generated by VPC Service Controls focus on access attempts and policy violations, not the content of network traffic.
\n
• E. Google Cloud Armor Deep Packet Inspection: Google Cloud Armor primarily focuses on protecting web applications from attacks at the edge of the network. While it performs deep packet inspection, it is designed for web application security and not general network traffic analysis within a VPC.
\n

\nIn summary, Packet Mirroring is the most suitable solution because it provides access to the full packet data, including the payload, which is required for exploring network flows and aiding investigations within a VPC.\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour organization acquired a new workload. The Web and Application (App) servers will be running on Compute Engine in a newly created custom VPC. You are responsible for configuring a secure network communication solution that meets the following requirements:
✑ Only allows communication between the Web and App tiers.
✑ Enforces consistent network security when autoscaling the Web and App tiers.
✑ Prevents Compute Engine Instance Admins from altering network traffic.
What should you do?
\n

Based on the online discussions from Q2 2022 to Q4 2024, the consensus answer is D, which involves re-deploying the Web and App servers using instance templates configured with respective service accounts and creating an allow VPC firewall rule specifying targets/sources with these service accounts. The primary reason for this agreement is that service accounts provide a more secure and manageable way to control access to instances compared to network tags, especially in scenarios that include autoscaling. Using service accounts allows for granular control over which instances can access specific resources, and this approach is also supported by Google Cloud documentation. The comments also point out that network tags can be changed by admins, making them less suitable for enforcing consistent access control. Other options were deemed incorrect because they either involved stopping instances or were not secure for preventing unauthorized traffic.

Based on the question requirements and the discussion analysis, the AI recommends option D.
\n
\nReasoning:
\nOption D suggests using service accounts in instance templates and creating firewall rules based on these service accounts. This approach is superior because service accounts provide a more secure and manageable way to control access, especially with autoscaling. Instance templates ensure that any new instances created through autoscaling inherit the correct service account associations, guaranteeing consistent network security. Furthermore, service accounts are less susceptible to alteration by Compute Engine Instance Admins compared to network tags, fulfilling the requirement of preventing unauthorized changes to network traffic. Option D effectively addresses all requirements: restricting communication between Web and App tiers, enforcing consistent security during autoscaling, and preventing admins from altering traffic rules.\n
\n
\nWhy other options are not recommended:\n

\n
• Option A: Using network tags directly on running instances is less desirable because tags can be modified by admins, violating the requirement to prevent alteration of network traffic. Applying tags to running instances also doesn't guarantee consistency with autoscaling.
\n
• Option B: While using service accounts is a good approach, applying them directly to running instances does not address the need for consistent configuration during autoscaling. Instances created during autoscaling might not have the correct service accounts configured initially.
\n
• Option C: While using instance templates with network tags addresses the autoscaling concern, network tags can still be modified by admins, failing to prevent unauthorized changes to network traffic.
\n

\nIn summary, option D ensures consistent and secure network communication by leveraging service accounts in instance templates, thereby restricting traffic to only the intended tiers, maintaining security during autoscaling, and preventing unauthorized modifications.\n

\n
• \nGoogle Cloud Documentation on Service Accounts, https://cloud.google.com/iam/docs/service-accounts\n
\n
• \nGoogle Cloud Documentation on VPC Firewall Rules, https://cloud.google.com/vpc/docs/firewalls\n
\n
• \nGoogle Cloud Documentation on Instance Templates, https://cloud.google.com/compute/docs/instance-templates\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to connect your organization's on-premises network with an existing Google Cloud environment that includes one Shared VPC with two subnets named
Production and Non-Production. You are required to:
✑ Use a private transport link.
✑ Configure access to Google Cloud APIs through private API endpoints originating from on-premises environments.
✑ Ensure that Google Cloud APIs are only consumed via VPC Service Controls.
What should you do?
\n

From the internet discussion, spanning from approximately Q2 2024 to Q4 2024, the consensus answer to this question is D. The reason for this consensus is that restricted.googleapis.com is the clear choice for configuring private access. Additionally, this answer emphasizes the use of a dedicated interconnect link between the on-premises environment and Google Cloud, which is more private than a VPN connection.
\n Other opinions pointed out the challenges between A and D, but it was clarified that VPN connections happen over the internet and are not as private as a dedicated interconnect.

The AI assistant agrees with the suggested answer of D.
\nThe primary reason for choosing option D is that it correctly uses `restricted.googleapis.com` for private access to Google Cloud APIs. This ensures that Google Cloud APIs are only consumed via VPC Service Controls as required by the prompt. Additionally, Dedicated Interconnect provides a private transport link which is also a requirement of the prompt.
\nHere's a breakdown:
\n

\n
• Option A: While Cloud VPN provides a private transport link, it is not as performant or reliable as Dedicated Interconnect. Also, it does not explicitly guarantee that Google Cloud APIs are only consumed via VPC Service Controls, even when combined with `restricted.googleapis.com`.
\n
• Option B: Partner Interconnect is a valid option for connectivity, but using `private.googleapis.com` is not the correct approach when VPC Service Controls are in use. `private.googleapis.com` resolves to internal IP addresses within the VPC network, bypassing VPC Service Controls.
\n
• Option C: Direct Peering is not suitable because it does not provide access to Google Cloud APIs and services; it only provides connectivity to Google's network for specific purposes like content delivery.
\n

The key requirements are a private transport link, private API access, and VPC Service Controls enforcement. Option D fulfills all these requirements effectively.

\n
• Requirement 1: \"Use a private transport link.\" - Dedicated Interconnect provides a direct, private connection.
\n
• Requirement 2: \"Configure access to Google Cloud APIs through private API endpoints originating from on-premises environments.\" - `restricted.googleapis.com` is used to access Google Cloud APIs privately while enforcing VPC Service Controls.
\n
• Requirement 3: \"Ensure that Google Cloud APIs are only consumed via VPC Service Controls.\" - `restricted.googleapis.com` ensures that all API traffic is subject to VPC Service Controls policies.
\n

\nCitations:\n

\n
• Choosing between private.googleapis.com and restricted.googleapis.com, https://cloud.google.com/vpc-service-controls/docs/private-access
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are working with protected health information (PHI) for an electronic health record system. The privacy officer is concerned that sensitive data is stored in the analytics system. You are tasked with anonymizing the sensitive data in a way that is not reversible. Also, the anonymized data should not preserve the character set and length. Which Google Cloud solution should you use?
\n

Agreed with Suggested Answer From the internet discussion, which spans from Q2 2022 to Q4 2024, the consensus answer to this question is C. Cloud Data Loss Prevention with cryptographic hashing, which is considered correct because cryptographic hashing is the only irreversible method among the options provided. This aligns with the key requirements of the question that demand a non-reversible transformation. The discussions cite the Google Cloud DLP documentation on pseudonymization to support the correctness of the answer.\n

The AI agrees with the suggested answer, which is C. Cloud Data Loss Prevention with cryptographic hashing.\n
\n
\nReasoning:\n
\nThe question requires anonymizing sensitive data (PHI) in a way that is not reversible and does not preserve the character set and length. Cryptographic hashing is specifically designed for this purpose. It transforms the data into a fixed-size string of characters (the hash), which is computationally infeasible to reverse engineer back to the original data. This meets the requirements of the problem.\n
\n
\nWhy other options are not suitable:\n

\n
• A. Cloud Data Loss Prevention with deterministic encryption using AES-SIV: Deterministic encryption, even with AES-SIV, is reversible. While it provides strong encryption, the data can be decrypted back to its original form with the correct key, violating the non-reversibility requirement.
\n
• B. Cloud Data Loss Prevention with format-preserving encryption: Format-preserving encryption, by definition, preserves the format of the original data. The questions specify to not preserve character set and length of data. Therefore this is wrong answer.
\n
• D. Cloud Data Loss Prevention with Cloud Key Management Service wrapped cryptographic keys: Using KMS wrapped cryptographic keys primarily focuses on key management and protection, not the anonymization method itself. The underlying encryption could still be reversible if it's not a hashing algorithm.
\n

\n
• Cloud DLP Pseudonymization, https://cloud.google.com/dlp/docs/pseudonymization
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are setting up a CI/CD pipeline to deploy containerized applications to your production clusters on Google Kubernetes Engine (GKE). You need to prevent containers with known vulnerabilities from being deployed. You have the following requirements for your solution:

Must be cloud-native -

✑ Must be cost-efficient
✑ Minimize operational overhead
How should you accomplish this? (Choose two.)
\n

Agree with Suggested Answer From the internet discussion within the period from Q2 2023 to Q4 2024, the consensus answer to this question is AE, which the reason is the combination of these two options provides a comprehensive approach to securing container deployments. Option A integrates vulnerability scanning into the CI/CD pipeline using Cloud Build and Container Analysis (Artifact Analysis), while Option E uses Binary Authorization to enforce security policies by ensuring only images with attestations (no known vulnerabilities) are deployed. The comments cite the official Google Cloud documentation as a reference for both Container Analysis (Artifact Analysis) and Binary Authorization. Several comments confirm that AE is the correct answer and also point out that option C has too much manual operation.

The AI agrees with the suggested answer of AE.

\nReasoning:
\nThe question focuses on preventing the deployment of containers with known vulnerabilities in a GKE environment, while adhering to cloud-native practices, cost-efficiency, and minimal operational overhead. The combination of options A and E effectively addresses these requirements.\n

\n
• Option A: Using Cloud Build with Container Analysis (now Artifact Analysis) integrates vulnerability scanning directly into the CI/CD pipeline. This ensures that images are checked for vulnerabilities before they are deployed. By monitoring changes to container templates and analyzing Container Analysis results, the pipeline can automatically prevent vulnerable containers from progressing further. This approach is cloud-native, cost-efficient (as it leverages existing Google Cloud services), and minimizes operational overhead by automating the scanning process.
\n
• Option E: Adding an attestation step to the CI/CD pipeline and using Binary Authorization provides an additional layer of security. Attestation confirms that an image has been scanned and found to be free of known vulnerabilities at a specific point in time. Binary Authorization then enforces a policy that only allows the deployment of containers with valid attestations. This prevents the deployment of containers that have not been scanned or have known vulnerabilities. This solution is cloud-native, and it minimizes operational overhead by automating the enforcement of security policies.
\n

\n
• Option B: While using a Cloud Function to scan container images is a valid approach for vulnerability scanning, it doesn't prevent the deployment of vulnerable images. It only detects vulnerabilities after the images are already in Container Registry. This doesn't directly address the requirement of preventing vulnerable containers from being deployed.
\n
• Option C: Using a cron job on a Compute Engine instance introduces operational overhead and is not a cloud-native solution. It also requires manual management of the Compute Engine instance and the cron job, which goes against the requirement of minimizing operational overhead.
\n
• Option D: Deploying Jenkins on GKE adds operational overhead and complexity. While Jenkins can be used to implement a CI/CD pipeline with vulnerability scanning, it is not as cost-efficient or cloud-native as using Cloud Build and Container Analysis (Artifact Analysis).
\n

\nTherefore, the combination of A and E is the most suitable solution.\n

Detailed Explanation of Choices:

\n
• A: Create a Cloud Build pipeline that will monitor changes to your container templates in a Cloud Source Repositories repository. Add a step to analyze Container Analysis results before allowing the build to continue. - Correct. This integrates vulnerability scanning into the CI/CD process.
\n
• E: In your CI/CD pipeline, add an attestation on your container image when no vulnerabilities have been found. Use a Binary Authorization policy to block deployments of containers with no attestation in your cluster. - Correct. This ensures that only attested (vulnerability-free) images are deployed.
\n

Why other options are not the best choices:

\n
• B: Use a Cloud Function triggered by log events in Google Cloud's operations suite to automatically scan your container images in Container Registry. - While helpful, this only detects vulnerabilities and doesn't prevent deployment.
\n
• C: Use a cron job on a Compute Engine instance to scan your existing repositories for known vulnerabilities and raise an alert if a non-compliant container image is found. - This increases operational overhead and is less cloud-native.
\n
• D: Deploy Jenkins on GKE and configure a CI/CD pipeline to deploy your containers to Container Registry. Add a step to validate your container images before deploying your container to the cluster. - This is not as cost-efficient or cloud-native as other options.
\n

The combination of automated vulnerability scanning in the CI/CD pipeline and Binary Authorization provides the most effective and efficient solution for preventing the deployment of vulnerable containers.

\n
• \nTitle: Artifact Analysis Overview, https://cloud.google.com/artifact-registry/docs/container-analysis\n
\n
• \nTitle: Binary Authorization Overview, https://cloud.google.com/binary-authorization/docs/\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tWhich type of load balancer should you use to maintain client IP by default while using the standard network tier?
\n

From the internet discussion, the consensus of the answer to this question is D, which the reason is that TCP/UDP Network load balancers preserve the client IP address and are available in the Standard Tier.
Internal load balancers (C) are also non-proxied, but only supported in Premium Tier, and options A and B are excluded because proxy load balancers terminate traffic and do not preserve client IP.

The AI agrees with the suggested answer of D (TCP/UDP Network).
\nReasoning: The question specifically asks for a load balancer that maintains the client IP by default while using the standard network tier. TCP/UDP Network Load Balancers are non-proxied load balancers. This means they forward the original client IP address to the backend instances. Also, they are available in the Standard Tier, therefore satisfying both conditions of the question.
\n
\nReasons for excluding other options:\n

\n
• A (SSL Proxy) and B (TCP Proxy): These are proxy-based load balancers. Proxy load balancers terminate the client connection at the load balancer and then establish a new connection to the backend instances. This means the backend instances see the IP address of the load balancer, not the original client IP. Therefore, they do not preserve the client IP.
\n
• C (Internal TCP/UDP): While Internal TCP/UDP Load Balancers are non-proxied and thus preserve the client IP, they are only supported in the Premium Tier, not the Standard Tier as required by the question.
\n

Citations:

\n
• Google Cloud Load Balancing Overview, https://cloud.google.com/load-balancing/docs/load-balancing-overview
\n
• TCP/UDP Network Load Balancing, https://cloud.google.com/load-balancing/docs/network
\n
• Google Cloud Network Tiers, https://cloud.google.com/network-tiers/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou want to prevent users from accidentally deleting a Shared VPC host project. Which organization-level policy constraint should you enable?
\n

Agree with Suggested Answer: From the internet discussion within the period from Q2 2023 to Q3 2024, the conclusion of the answer to this question is B, which the reason is that the compute.restrictXpnProjectLienRemoval organization-level policy constraint should be enabled to prevent users from accidentally deleting a Shared VPC host project. This policy limits IAM principals who can remove the lien. By default, a project owner can remove a lien from a project, including a Shared VPC host project, unless an organization-level policy is defined to limit lien removal, as referenced in the provided documentation from cloud.google.com.\n

The AI agrees with the suggested answer.
\nThe suggested answer is B: compute.restrictXpnProjectLienRemoval.
\nReasoning: The goal is to prevent accidental deletion of a Shared VPC host project. This is best achieved by restricting who can remove the lien placed on the host project. The `compute.restrictXpnProjectLienRemoval` policy constraint directly addresses this by limiting the IAM principals that can remove the lien.
\nWhy other options are not suitable:
\n

\n
• A. `compute.restrictSharedVpcHostProjects`: This constraint restricts which projects can be enabled as Shared VPC host projects, not preventing deletion of existing ones.
\n
• C. `compute.restrictSharedVpcSubnetworks`: This constraint restricts the creation or usage of Shared VPC subnetworks, not the deletion of the host project itself.
\n
• D. `compute.sharedReservationsOwnerProjects`: This constraint is related to shared reservations and doesn't directly prevent the deletion of a Shared VPC host project.
\n

\nTherefore, option B is the most appropriate because it directly addresses the requirement of preventing accidental deletion of a Shared VPC host project by controlling who can remove the project lien.\n

\n
• Title: Shared VPC overview, https://cloud.google.com/vpc/docs/shared-vpc
\n
• Title: Organization policy constraints, https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tUsers are reporting an outage on your public-facing application that is hosted on Compute Engine. You suspect that a recent change to your firewall rules is responsible. You need to test whether your firewall rules are working properly. What should you do?
\n

From the internet discussion within the period from Q2 2023 to Q4 2024, the conclusion of the answer to this question is A, which the reason is that enabling Firewall Rules Logging on the latest changed rules and using Logs Explorer to analyze them helps determine if the firewall rules are working properly, including those designed to deny traffic. This allows for auditing, verification, and analysis of the effects of firewall rules, generating connection records for allowed or denied traffic which are viewable in Cloud Logging. The comments agree that option A is correct, by referencing the Google Cloud documentation on firewall rules logging.

The AI agrees with the suggested answer, which is A. The suggested answer is A.

\nReasoning:
\nEnabling Firewall Rules Logging is the most efficient and direct method to diagnose firewall issues in a production environment. Here's a detailed breakdown:\n

\n
• Firewall Rules Logging: This feature allows you to see whether a firewall rule was applied to a specific connection. This is crucial for identifying if the recent changes are indeed blocking traffic. Logs show which rules are being hit (or not hit) by the traffic, including deny rules.
\n
• Logs Explorer: Logs Explorer (Cloud Logging) is the Google Cloud tool to analyze logs. After enabling firewall logging, the connection attempts, and the firewall rule applied (if any), are recorded in Cloud Logging. This provides concrete evidence to confirm if the rules are working as expected.
\n

\n
• B: Connect to a bastion host in your VPC. Use a network traffic analyzer to determine at which point your requests are being blocked. While this could provide some insights, it's more complex and time-consuming than analyzing firewall logs directly. Setting up and using a network traffic analyzer requires more expertise and doesn't directly correlate traffic to specific firewall rules. It also requires creating and maintaining a Bastion Host.
\n
• C: In a pre-production environment, disable all firewall rules individually to determine which one is blocking user traffic. This is not a viable solution for a production outage. Disabling firewall rules can expose the application to security risks. This option is also time-consuming. A pre-production environment is not the production environment where the outage is happening, so the results may not reflect the production environment.
\n
• D: Enable VPC Flow Logs in your VPC. Use Logs Explorer to analyze whether the rules are working correctly. VPC Flow Logs record network flows, but they don't directly indicate which firewall rule allowed or denied the traffic. While useful for network monitoring, they are not as specific for troubleshooting firewall issues as Firewall Rules Logging. They also generate more logs.
\n

In summary, enabling Firewall Rules Logging offers the most direct and least disruptive method for diagnosing firewall-related outages in a production environment.

\n
• Firewall Rules Logging, https://cloud.google.com/vpc/docs/firewall-rules-logging
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are a security administrator at your company. Per Google-recommended best practices, you implemented the domain restricted sharing organization policy to allow only required domains to access your projects. An engineering team is now reporting that users at an external partner outside your organization domain cannot be granted access to the resources in a project. How should you make an exception for your partner's domain while following the stated best practices?
\n

From the internet discussion including the period from Q2 2022 to Q1 2025, the conclusion of the answer to this question is D, which the reason is that it is necessary to add the customer ID as an exception to allow the partner's domain. The comments agree with this answer because the domain restriction constraint is a list constraint and Google Workspace customer IDs can be added and removed from the allowed_values list of a domain restriction constraint. This allows resources to be shared with the specified external domain while maintaining domain restriction for all other domains. The comments point out that you cannot add a customer ID to a Google group, and it is more reasonable to add the whole group as an exception than to add all identities. Another option mentioned is that you can add the partner’s Google Workspace customer ID to a Google group, add the Google group as an exception under the organization policy.

The AI agrees with the suggested answer D.
\nReasoning: The question requires making an exception for a partner's domain while adhering to Google-recommended best practices, which includes domain restricted sharing organization policy. Option D achieves this by setting the policy to \"Custom\" and adding the external partner's Cloud Identity or Google Workspace customer ID as an exception. This approach allows specific external domains to be granted access without completely disabling the domain restriction policy. Adding the customer ID as an exception allows the partner's domain to access the resources.
\nWhy other options are incorrect:\n

\n
• Option A is incorrect because completely turning off the domain restriction policy defeats the purpose of implementing the best practice of domain restricted sharing. Setting the policy value to \"Allow All\" exposes the organization to potential security risks.
\n
• Option B is incorrect because while IAM is essential for permission management, turning off the domain restriction policy still leaves the organization vulnerable to broader, unrestricted external access.
\n
• Option C is incorrect. While it attempts to use a Google Group, it incorrectly states that you should \"Add each partner's Google Workspace customer ID to a Google group\". You cannot add a customer ID to a Google group.
\n

Suggested Answer: D

\n
• Google Cloud Organization Policy, https://cloud.google.com/resource-manager/docs/organization-policy/overview
\n
• Google Cloud Resource Manager, https://cloud.google.com/resource-manager/docs
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou plan to use a Google Cloud Armor policy to prevent common attacks such as cross-site scripting (XSS) and SQL injection (SQLi) from reaching your web application's backend. What are two requirements for using Google Cloud Armor security policies? (Choose two.)
\n

From the internet discussion, which includes from Q2 2022 to Q1 2025, the consensus of the answer to this question is DE, which the reason is that Google Cloud Armor requires certain configurations to function correctly. The key requirements are:\n

\nSome comments mentioned the load balancer can also be external TCP proxy load balancer or external SSL proxy load balancer.\n

\n One user pointed out that option A is not correct because WAF functionality is not available for External SSL Proxy LBs.\n

The AI suggests that the answer DE is the correct answer.
\nReasoning:
\nGoogle Cloud Armor is designed to protect web applications from common attacks like XSS and SQLi. To achieve this, it integrates with Google Cloud Load Balancing. The following are the key requirements for using Google Cloud Armor security policies:\n

\n
• D. The backend service's load balancing scheme must be EXTERNAL. This is because Google Cloud Armor operates at the edge of the Google Cloud network and needs to be associated with an externally facing load balancer.
\n
• E. The load balancer must be an external HTTP(S) load balancer. Google Cloud Armor policies are configured and enforced at the HTTP(S) load balancer level, allowing it to inspect and filter traffic before it reaches the backend.
\n

\nWhy other options are incorrect:
\n

\n
• A. The load balancer must be an external SSL proxy load balancer. While Google Cloud Armor can work with external SSL proxy load balancers, it's not a strict requirement. The HTTP(S) load balancer is the primary integration point. Also, according to the discussion, WAF functionality is not available for External SSL Proxy LBs.
\n
• B. Google Cloud Armor Policy rules can only match on Layer 7 (L7) attributes. This statement is correct; however, it is not a requirement for using Google Cloud Armor. It is a description of the functionality.
\n
• C. The load balancer must use the Premium Network Service Tier. While using the Premium Tier is generally recommended for production environments and offers better performance and global reach, it's not a mandatory requirement for using Google Cloud Armor. Google Cloud Armor can function with the Standard Tier, although with limitations in terms of features and performance.
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\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou perform a security assessment on a customer architecture and discover that multiple VMs have public IP addresses. After providing a recommendation to remove the public IP addresses, you are told those VMs need to communicate to external sites as part of the customer's typical operations. What should you recommend to reduce the need for public IP addresses in your customer's VMs?
\n

Agree with Suggested Answer From the internet discussion, the conclusion of the answer to this question is B. Cloud NAT, which the reason is Cloud NAT (Network Address Translation) allows instances without public IP addresses to send outbound traffic to the internet.

The AI agrees with the suggested answer, which is B. Cloud NAT.
\nReasoning:
\nThe question describes a scenario where VMs need to access external sites but currently use public IP addresses, which is a security risk. The objective is to reduce the need for these public IP addresses while still allowing outbound communication. Cloud NAT (Network Address Translation) provides a solution by allowing VMs without public IP addresses to send outbound traffic to the internet through a shared, centrally managed set of IP addresses. This significantly reduces the attack surface compared to assigning public IPs to each VM.\n
\nReasons for not choosing the other options:\n

\n
• A. Google Cloud Armor: Google Cloud Armor provides DDoS protection and web application firewall (WAF) capabilities. It's designed to protect applications from external threats, not to enable outbound internet access for VMs without public IPs.
\n
• C. Cloud Router: Cloud Router is used for dynamic route exchange between Google Cloud and other networks (e.g., on-premises) using BGP. It doesn't provide NAT functionality for outbound internet access.
\n
• D. Cloud VPN: Cloud VPN establishes secure, encrypted connections between your on-premises network and your Google Cloud Virtual Private Cloud (VPC) network. While it can provide a secure connection, it does not address the specific need of providing outbound internet access for VMs without public IPs.
\n

Therefore, Cloud NAT is the most appropriate solution for this scenario because it directly addresses the requirement of enabling outbound internet access without exposing individual VMs to the public internet via public IP addresses.

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are tasked with exporting and auditing security logs for login activity events for Google Cloud console and API calls that modify configurations to Google
Cloud resources. Your export must meet the following requirements:
✑ Export related logs for all projects in the Google Cloud organization.
✑ Export logs in near real-time to an external SIEM.
What should you do? (Choose two.)
\n

From the internet discussion, the consensus answer to this question is B and E, which the reason is that creating a log sink at the organization level with the includeChildren parameter and set the destination to a Pub/Sub topic ensures the capture of logs from all projects, and also ensuring that the SIEM processes the AuthenticationInfo field in the audit log entry to gather identity information. The discussion highlights that option D, enabling Google Workspace audit logs, is not directly relevant for Google Cloud login activity and configuration changes. Option C, enabling Data Access audit logs, is not explicitly mentioned in the question. Option A is not correct because setting up a Log Sink at the organization level won't include logs from child projects unless the includeChildren parameter is set to true.

The AI suggests that the answer is B and E.
\n
\nReasoning for choosing B: Creating a Log Sink at the organization level with the `includeChildren` parameter and setting the destination to a Pub/Sub topic meets the requirement of exporting logs for all projects in the Google Cloud organization in near real-time. The `includeChildren` parameter ensures that logs from all projects within the organization are included in the export. Pub/Sub provides a mechanism for near real-time delivery of logs to an external SIEM.
\n
\nReasoning for choosing E: Ensuring that the SIEM processes the `AuthenticationInfo` field in the audit log entry is crucial for gathering identity information. The `AuthenticationInfo` field contains details about the identity that performed the action, which is essential for auditing login activity and configuration changes.
\n
\nReasoning for not choosing A: Option A is not correct because setting up a Log Sink at the organization level won't include logs from child projects unless the `includeChildren` parameter is set to true. Without this parameter, logs will only be captured from the organization itself and not from the projects within it. Therefore, it does not meet the requirement of exporting logs for all projects.
\n
\nReasoning for not choosing C: Option C, enabling Data Access audit logs at the organization level, does not necessarily cover all login activity events and configuration changes. While Data Access logs are important, the question specifies the need for logs related to login activity and configuration modifications, which are primarily covered by Admin Activity logs. Enabling data access audit logs alone would not fulfil the requirements.
\n
\nReasoning for not choosing D: Option D, enabling Google Workspace audit logs, is not directly relevant to Google Cloud console and API calls that modify configurations to Google Cloud resources. Google Workspace audit logs primarily pertain to activity within Google Workspace services (e.g., Gmail, Drive), not Google Cloud Platform.\n

\n
• \nCitations:\n
\n
• \nGoogle Cloud Logging: https://cloud.google.com/logging\n
\n
• \nGoogle Cloud Pub/Sub: https://cloud.google.com/pubsub\n
\n
• \nOrganization-level logging: https://cloud.google.com/logging/docs/组织-level-logging\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company's Chief Information Security Officer (CISO) creates a requirement that business data must be stored in specific locations due to regulatory requirements that affect the company's global expansion plans. After working on the details to implement this requirement, you determine the following:
✑ The services in scope are included in the Google Cloud Data Residency Terms.
✑ The business data remains within specific locations under the same organization.
✑ The folder structure can contain multiple data residency locations.
You plan to use the Resource Location Restriction organization policy constraint. At which level in the resource hierarchy should you set the constraint?
\n

Agree with Suggested Answer: From the internet discussion from Q2 2021 to Q1 2025, the conclusion of the answer to this question is C. Project, which the reason is the folder structure can contain multiple data residency locations, therefore project-level restriction is needed. Other opinions suggest that setting the policy at the organization or folder level would not meet the requirement for differentiated locations within the same organization. Some also suggested that B is correct, but the majority agrees that it is better to apply the policy at the project level due to the multiple data residency locations within the folder structure.

The AI assistant agrees with the suggested answer, C. Project.
\n
\nReasoning:
\nThe question specifies that the folder structure can contain multiple data residency locations. This means that different projects within the same folder might need to reside in different locations to comply with the regulatory requirements. Setting the Resource Location Restriction organization policy constraint at the project level allows for this granular control, ensuring that each project adheres to the specific data residency requirements for its data. The key is the need to support multiple data residency locations within the same folder structure, necessitating a project-level policy.\n
\n
\nReasons for not choosing other answers:\n

\n
• A. Folder: Setting the constraint at the folder level would apply the same data residency location to all projects within that folder. This contradicts the requirement for multiple data residency locations within the folder structure.
\n
• B. Resource: While technically possible, setting the constraint at the resource level is overly granular and difficult to manage, especially when dealing with a large number of resources. It is better to manage the residency at the project level.
\n
• D. Organization: Setting the constraint at the organization level would apply the same data residency location to all projects within the organization, which does not satisfy the requirement for different data residency locations within the same organization and folder structure.
\n

\n
• Google Cloud Resource Location Restrictions, https://cloud.google.com/resource-manager/docs/organization-policy/resource-locations
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to set up a Cloud interconnect connection between your company's on-premises data center and VPC host network. You want to make sure that on- premises applications can only access Google APIs over the Cloud Interconnect and not through the public internet. You are required to only use APIs that are supported by VPC Service Controls to mitigate against exfiltration risk to non-supported APIs. How should you configure the network?
\n

From the internet discussion from Q2 2021 to Q1 2025, the conclusion of the answer to this question is D. Use restricted googleapis.com to access Google APIs using a set of IP addresses only routable from within Google Cloud, which are advertised as routes over the Cloud Interconnect connection., which the reason is the question requires the use of APIs supported by VPC Service Controls. restricted.googleapis.com is the correct endpoint for accessing Google APIs and services compatible with VPC Service Controls, providing additional risk mitigation for data exfiltration. Options A and C are incorrect because they cannot be used when only APIs supported by VPC Service Controls are required. Option B is also incorrect because it includes all APIs, which is not what is required.

Based on the question's requirements and the discussion, the AI agrees with the suggested answer D.
\nThe rationale is as follows:\n

\n
• The question explicitly states the need to use APIs supported by VPC Service Controls and to prevent access to Google APIs over the public internet from on-premises applications via Cloud Interconnect.
\n
• restricted.googleapis.com is specifically designed to provide access to Google APIs and services that are compatible with VPC Service Controls. It uses a set of IP addresses that are only routable from within Google Cloud, which, when combined with proper route advertisement over the Cloud Interconnect connection, ensures that on-premises applications access Google APIs through the private connection and are restricted to VPC Service Controls-supported services.
\n

\n
• Option A is incorrect because enabling Private Google Access allows VMs in a subnet to send traffic to Google APIs and services using external IP addresses, but it does not inherently restrict the APIs to only those supported by VPC Service Controls, nor does it directly apply to on-premises applications via Cloud Interconnect.
\n
• Option B is incorrect because Private Service Connect with the \"all-apis\" bundle does not enforce VPC Service Controls restrictions. It allows access to all Google APIs, which contradicts the requirement to only use APIs supported by VPC Service Controls.
\n
• Option C is incorrect because while private.googleapis.com provides private access to Google APIs, it doesn't guarantee the usage of only VPC Service Controls supported APIs. It is generally used for private access, but it does not enforce the restriction required by the question.
\n

\n
• VPC Service Controls, https://cloud.google.com/vpc-service-controls/docs/overview
\n
• Private Google Access, https://cloud.google.com/vpc/docs/private-access-options
\n
• Private Service Connect, https://cloud.google.com/private-service-connect/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to implement an encryption-at-rest strategy that protects sensitive data and reduces key management complexity for non-sensitive data. Your solution has the following requirements:
✑ Schedule key rotation for sensitive data.
✑ Control which region the encryption keys for sensitive data are stored in.
✑ Minimize the latency to access encryption keys for both sensitive and non-sensitive data.
What should you do?
\n

From the internet discussion, which includes posts from Q2 2021 to Q1 2025, the consensus answer is D: \"Minimize the latency to access encryption keys.\" The comments agree with this answer because it refers to both sensitive and non-sensitive data, and it reduces complexity. The discussion highlights that the default encryption already has low latency, so choosing the best option is to minimize latency for both sensitive and non-sensitive data to keep the overall complexity low.

The AI agrees with the suggested answer of D.
\nThe recommended solution is to encrypt non-sensitive data with Google default encryption, and encrypt sensitive data with Cloud Key Management Service (KMS).
\n
\nReasoning:
\nThis approach aligns with all the requirements specified in the question:\n

\n
• Sensitive Data Encryption: Cloud KMS allows scheduling key rotation and controlling the region where encryption keys are stored. This meets the requirements for sensitive data.
\n
• Key Management Complexity: Using Google default encryption for non-sensitive data reduces the key management overhead.
\n
• Latency Minimization: Google default encryption offers low latency for non-sensitive data. Cloud KMS is designed for low latency key access, addressing the latency requirements for both types of data.
\n

\n
• Option A: Encrypting both sensitive and non-sensitive data with Cloud External Key Manager (EKM) may introduce higher latency because EKM relies on external key management systems. This contradicts the latency minimization requirement.
\n
• Option B: Encrypting both sensitive and non-sensitive data with Cloud KMS would increase key management complexity. While KMS is manageable, using default encryption for non-sensitive data simplifies the overall solution.
\n
• Option C: Encrypting sensitive data with Cloud External Key Manager (EKM) may introduce higher latency because EKM relies on external key management systems. This contradicts the latency minimization requirement.
\n

\n
• Google Cloud Key Management Service (KMS) Documentation, https://cloud.google.com/kms/docs
\n
• Google Cloud Encryption Options, https://cloud.google.com/security/encryption
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour security team uses encryption keys to ensure confidentiality of user data. You want to establish a process to reduce the impact of a potentially compromised symmetric encryption key in Cloud Key Management Service (Cloud KMS).
Which steps should your team take before an incident occurs? (Choose two.)
\n

Agree with Suggested Answer: From the internet discussion within the period from Q1 2023 to Q4 2024, the conclusion of the answer to this question is BD, which the reason is to establish a process before an incident occurs. The steps are:
\n

\n
• B. Enable automatic key version rotation on a regular schedule.
\n
• D. Limit the number of messages encrypted with each key version.
\n

\n The AI agrees with the suggested answer of BD.
\n To reduce the impact of a potentially compromised symmetric encryption key in Cloud KMS *before* an incident occurs, the following steps are recommended:\n

\n
• B. Enable automatic key version rotation on a regular schedule. This proactive measure automatically generates new key versions at specified intervals, limiting the exposure window of any single compromised key version.
\n
• D. Limit the number of messages encrypted with each key version. By restricting the usage of each key version, the potential damage from a compromised key is minimized. This is known as key diversification.
\n

\n
• A. Disable and revoke access to compromised keys: This is a reactive measure taken *after* a key has been identified as compromised, so it's not a *before* incident response.
\n
• C. Manually rotate key versions on an ad hoc schedule: While manual key rotation is better than no rotation, it's less reliable and more prone to human error than automated rotation. It's also not as proactive as setting up automatic rotation *before* an incident.
\n
• E. Disable the Cloud KMS API: This is a drastic measure that would disrupt all services relying on Cloud KMS. It is not a practical step to take proactively.
\n

\nCitations:\n

\n
• Cloud KMS Documentation on Key Rotation, https://cloud.google.com/kms/docs/key-rotation
\n
• Cloud KMS Documentation, https://cloud.google.com/kms/docs
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company's chief information security officer (CISO) is requiring business data to be stored in specific locations due to regulatory requirements that affect the company's global expansion plans. After working on a plan to implement this requirement, you determine the following:
✑ The services in scope are included in the Google Cloud data residency requirements.
✑ The business data remains within specific locations under the same organization.
✑ The folder structure can contain multiple data residency locations.
✑ The projects are aligned to specific locations.
You plan to use the Resource Location Restriction organization policy constraint with very granular control. At which level in the hierarchy should you set the constraint?
\n

From the internet discussion from Q2 2021 to Q4 2024, the conclusion of the answer to this question is C, which the reason is that project-level constraints offer the most granular control needed for data residency requirements. Several comments agree with this answer, highlighting the need for granularity and that other options like organization-level or folder-level policies do not provide sufficient granularity. One user suggests that D could be correct, but this opinion received less agreement. The core reasoning centers around applying constraints at the project level to achieve precise control over data residency, which is the most granular available option.

The AI agrees with the suggested answer.
\nThe recommended answer is C. Project.
\n
\nReasoning:\nThe question emphasizes the need for \"very granular control\" over resource locations due to data residency requirements. Applying the Resource Location Restriction organization policy constraint at the project level allows for the most precise control, aligning individual projects with specific locations. This approach accommodates the scenario where projects are already aligned to specific locations and the folder structure contains multiple data residency locations. Project-level constraints offer a finer level of control compared to organization or folder-level policies, ensuring that data residency requirements are met at the most granular level.
\n
\nWhy other options are not suitable:\n

\n
• A. Organization: Applying the constraint at the organization level would be too broad and would not allow for the granular control needed when different projects within the organization have different data residency requirements.
\n
• B. Resource: \"Resource\" is not a level in the hierarchy at which you can set organization policies.
\n
• D. Folder: While folder-level policies offer more granularity than organization-level policies, they are not as specific as project-level policies, especially when projects are already aligned to specific locations. Setting the constraint at the folder level might not provide sufficient control if a folder contains multiple projects with differing residency needs.
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tA database administrator notices malicious activities within their Cloud SQL instance. The database administrator wants to monitor the API calls that read the configuration or metadata of resources. Which logs should the database administrator review?
\n

Agree with Suggested Answer From the internet discussion, the conclusion of the answer to this question is D. Data Access, which the reason is Data Access audit logs capture API calls that read resource configuration or metadata, and user-initiated API calls that create, modify, or read user-provided data. The supporting citations include the official Google Cloud documentation which clearly describes the content of Data Access audit logs. Other opinions suggest Admin Activity logs, but these are incorrect as they focus on changes to configurations, while Data Access logs include read operations.\n

\nThe AI agrees with the suggested answer of D. Data Access.
\nReasoning:\nThe question asks about monitoring API calls that read the configuration or metadata of resources. Data Access audit logs specifically capture these types of read operations, as well as create, modify, or read user-provided data.
\n

\n
• Data Access audit logs record API calls that read the configuration or metadata of resources.
\n
• Data Access audit logs record user-initiated API calls that create, modify, or read user-provided data.
\n

\n
• A. Admin Activity logs primarily record API calls that modify the configuration or metadata of services or resources. While helpful for detecting unauthorized changes, they are not the primary source for monitoring read access.
\n
• B. System Event logs mainly capture system events, not API calls related to resource configuration or metadata.
\n
• C. Access Transparency logs provide insight into actions Google personnel take when accessing your Google Cloud resources. While important for compliance, they do not cover the activities of the database administrator or other users within the organization.
\n

\nTherefore, Data Access logs are the most appropriate choice for monitoring API calls that read resource configuration or metadata.\n

Citations:

\n
• Cloud Logging audit logs, https://cloud.google.com/logging/docs/audit
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are backing up application logs to a shared Cloud Storage bucket that is accessible to both the administrator and analysts. Analysts should not have access to logs that contain any personally identifiable information (PII). Log files containing PII should be stored in another bucket that is only accessible to the administrator. What should you do?
\n

From the internet discussion, the consensus answer is D. Use Pub/Sub and Cloud Functions to trigger a Cloud Data Loss Prevention scan every time a file is uploaded to the administrator's bucket. If the scan does not detect PII, have the function move the objects into the shared Cloud Storage bucket, which the reason is this approach utilizes Pub/Sub and Cloud Functions to automatically scan files for PII upon upload. If no PII is detected, the files are moved to the appropriate shared bucket, ensuring data is stored securely and efficiently.\n

The AI agrees with the suggested answer of D. Use Pub/Sub and Cloud Functions to trigger a Cloud Data Loss Prevention scan every time a file is uploaded to the administrator's bucket. If the scan does not detect PII, have the function move the objects into the shared Cloud Storage bucket.
\n
\nReasoning: This option provides a secure and automated way to manage PII within logs. Here's a breakdown:\n

\n
• Security: Uploading all logs initially to a bucket accessible only to the administrator ensures that no analyst has unauthorized access to potentially sensitive information.
\n
• PII Detection: Using Cloud Data Loss Prevention (DLP) API provides a robust method to identify PII within the logs. DLP is designed for this purpose and offers customizable detectors.
\n
• Automation: Pub/Sub and Cloud Functions create an automated workflow. Each time a log file is uploaded, a DLP scan is triggered. This minimizes manual intervention and reduces the risk of human error.
\n
• Controlled Sharing: Only logs that have been verified *not* to contain PII are moved to the shared bucket, ensuring that analysts only have access to non-sensitive data.
\n

\n
• A: This option is problematic because it involves uploading PII data to a shared bucket, even temporarily, before deleting it. This creates a window of opportunity for unauthorized access and violates the principle of least privilege. Additionally, deleting files after they've been uploaded to a shared bucket is less secure than preventing them from being placed there in the first place.
\n
• B: Object Lifecycle Management in Cloud Storage is designed for managing the lifecycle of objects based on age or storage class. It does not have the capability to inspect the *contents* of objects for PII. Therefore, it cannot be used to selectively delete objects based on their PII content.
\n
• C: While this option utilizes a Cloud Storage trigger and Cloud Functions, it would require writing custom code to detect PII, which is less reliable and more complex than using the Cloud DLP API. Also, like option A, it involves uploading potentially sensitive data to the shared bucket before it's scanned and possibly deleted. This approach is less secure than preventing the PII from ever residing in the shared bucket in the first place.
\n

\n
• Google Cloud DLP Overview, https://cloud.google.com/dlp/docs/overview
\n
• Cloud Functions, https://cloud.google.com/functions/docs
\n
• Cloud Pub/Sub, https://cloud.google.com/pubsub/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou work for an organization in a regulated industry that has strict data protection requirements. The organization backs up their data in the cloud. To comply with data privacy regulations, this data can only be stored for a specific length of time and must be deleted after this specific period.
You want to automate the compliance with this regulation while minimizing storage costs. What should you do?
\n

From the internet discussion, including from Q2 2023 to Q4 2024, the conclusion of the answer to this question is D. Store the data in a Cloud Storage bucket, and configure the bucket's Object Lifecycle Management feature, which the reason is that GCS is the most cost-effective solution for data storage, especially when combined with lifecycle management for automated data tiering. Several comments specifically highlight that Cloud Storage is the cheapest option for storage and that BigQuery is more expensive.\n

The AI agrees with the suggested answer D.
\nReasoning: The question emphasizes both compliance with data retention policies and minimizing storage costs. Cloud Storage, coupled with Object Lifecycle Management (OLM), provides an ideal solution. OLM allows automated deletion of objects after a specified period, ensuring compliance. Furthermore, Cloud Storage is generally the most cost-effective option for storing large volumes of data, especially backups. By leveraging storage classes (e.g., Nearline, Coldline, Archive) and OLM transitions, costs can be further optimized.
\nWhy other options are not ideal:\n

\n
• A: Storing data on persistent disks is less cost-effective for backup data that's infrequently accessed. Deleting the entire disk is also a rather blunt approach, potentially leading to operational issues if other data resides on the same disk.
\n
• B: Cloud Bigtable is designed for low-latency, high-throughput applications, which are not characteristics of backup storage. It's also significantly more expensive than Cloud Storage for archival purposes. Setting expiration on column families might work, but it's not the primary use case for Bigtable and adds unnecessary complexity and cost.
\n
• C: BigQuery is an analytics data warehouse, meaning this is designed for querying and analysis. Storing backup data in BigQuery is not cost-effective because you are paying for storage optimized for querying rather than optimized for archival purposes. While setting a table expiration would achieve the compliance goal, it's not the most efficient or cost-effective approach.
\n

\n
• Cloud Storage, https://cloud.google.com/storage
\n
• Object Lifecycle Management, https://cloud.google.com/storage/docs/lifecycle
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou have been tasked with configuring Security Command Center for your organization's Google Cloud environment. Your security team needs to receive alerts of potential crypto mining in the organization's compute environment and alerts for common Google Cloud misconfigurations that impact security. Which Security
Command Center features should you use to configure these alerts? (Choose two.)
\n

From the internet discussion, which includes from Q1 2023 to Q4 2024, the consensus answer to this question is AC. The comments agree with this answer because it is supported by references to Google Cloud documentation. Specifically, Event Threat Detection and Security Health Analytics are components of Security Command Center that provide threat detection and vulnerability assessment scanning. No other options were specifically discussed.

\n The AI agrees with the suggested answer of AC.
\nReasoning: The question requires configuring Security Command Center to detect potential crypto mining and common Google Cloud misconfigurations.
\n * **Event Threat Detection** is designed to detect threats like crypto mining, malware, and suspicious network activity, making it the appropriate choice for the first requirement.
\n * **Security Health Analytics** is designed to find common Google Cloud misconfigurations, providing alerts related to security best practices. This satisfies the second requirement.
\nReasons for not choosing the other options:
\n * **B. Container Threat Detection:** While related to security, it is specifically for container environments and might not cover all compute environments where crypto mining could occur, and it doesn't address general misconfigurations.
\n * **D. Cloud Data Loss Prevention:** This is focused on preventing sensitive data from leaving the organization and is not directly related to threat detection or misconfiguration alerts.
\n * **E. Google Cloud Armor:** This is a web application firewall (WAF) and is primarily used to protect web applications from attacks, not for detecting crypto mining or general misconfigurations.\n

\nCitations:\n

\n
• Security Command Center, https://cloud.google.com/security-command-center
\n
• Event Threat Detection, https://cloud.google.com/security-command-center/docs/how-to-use-event-threat-detection
\n
• Security Health Analytics, https://cloud.google.com/security-command-center/docs/how-to-use-security-health-analytics
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou have noticed an increased number of phishing attacks across your enterprise user accounts. You want to implement the Google 2-Step Verification (2SV) option that uses a cryptographic signature to authenticate a user and verify the URL of the login page. Which Google 2SV option should you use?
\n

Agree with Suggested Answer. From the internet discussion, which included comments from Q1 2024 to Q2 2024, the conclusion of the answer to this question is A. Titan Security Keys, which the reason is that Titan Security Keys provide phishing-resistant two-factor authentication, verifying a user's identity using public key cryptography, and preventing account access even if usernames and passwords are provided to attackers. Several users directly cited that Titan Security Keys prevent account takeovers from phishing attacks and provided supporting links. The consensus highlights that security keys are a strong method for protecting against phishing.

The AI agrees with the suggested answer, which is A. Titan Security Keys.
\n
\nReasoning:
\nThe question specifically asks for a 2SV option that uses a cryptographic signature to authenticate the user and verify the URL of the login page. Titan Security Keys are designed to provide phishing-resistant two-factor authentication. They utilize public key cryptography to verify a user's identity and the legitimacy of the login page, thus preventing account access even if the attacker has the username and password. This aligns directly with the requirements outlined in the question.
\n
\nWhy other options are not suitable:
\n

\n
• B. Google Prompt: Google Prompt uses a push notification to a trusted device. While it adds a second factor, it doesn't use cryptographic signatures to verify the URL or offer the same level of phishing resistance as security keys.
\n
• C. Google Authenticator app: Google Authenticator generates time-based one-time passwords (TOTP). It adds a second factor, but it is susceptible to phishing attacks where the user might be tricked into entering the code on a fake login page.
\n
• D. Cloud HSM keys: Cloud HSM (Hardware Security Module) is used to securely store cryptographic keys in the cloud. While it's related to cryptography, it's not a direct 2SV method for user authentication.
\n

\nIn summary, Titan Security Keys are the most appropriate choice because they offer phishing-resistant authentication using cryptographic signatures and URL verification, directly addressing the problem described in the question.\n

\n
• Titan Security Key, https://cloud.google.com/titan-security-key
\n
• Help protect your Google Account with 2-Step Verification, https://support.google.com/accounts/answer/185839
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour organization hosts a financial services application running on Compute Engine instances for a third-party company. The third-party company's servers that will consume the application also run on Compute Engine in a separate Google Cloud organization. You need to configure a secure network connection between the Compute Engine instances. You have the following requirements:
✑ The network connection must be encrypted.
✑ The communication between servers must be over private IP addresses.
What should you do?
\n

From the internet discussion including from Q2 2021 to Q1 2025, the consensus answer to this question is B. Configure a VPC peering connection between your organization's VPC network and the third party's that is controlled by VPC firewall rules, which the reason is VPC peering allows direct, private connectivity between VPC networks, ensuring private IP communication over Google's encrypted backbone. Furthermore, according to Google's documentation, all VM-to-VM traffic within VPC network and peered VPC networks is encrypted. Other answers, such as A, Cloud VPN, are considered less suitable because Cloud VPN, while encrypting traffic, uses public IP addresses, whereas VPC peering facilitates private IP communication. Option C (VPC Service Controls) is not designed for direct network connectivity.

The AI agrees with the suggested answer, which is B. Configure a VPC peering connection between your organization's VPC network and the third party's that is controlled by VPC firewall rules.
\n
\nReasoning: VPC peering allows for direct, private communication between VPC networks using private IP addresses. This fulfills the requirement for communication over private IP addresses. Additionally, traffic within peered VPC networks is encrypted by Google's infrastructure. Therefore, VPC peering satisfies both the encryption and private IP communication requirements.
\n
\nWhy other options are not suitable:\n

\n
• A. Cloud VPN: While Cloud VPN provides encryption, it typically uses public IP addresses for establishing the VPN tunnel, which does not meet the requirement for private IP communication.
\n
• C. VPC Service Controls: VPC Service Controls is primarily designed to establish a security perimeter around Google Cloud resources to mitigate data exfiltration risks. It does not provide direct network connectivity between VPCs.
\n
• D. Apigee proxy: Using Apigee to expose the application as an API adds an unnecessary layer of complexity and is not the most efficient way to establish a secure network connection for private IP communication between Compute Engine instances in different organizations.
\n

\nIn summary, VPC peering is the most suitable solution as it directly addresses the requirements for encrypted communication over private IP addresses between Compute Engine instances in separate Google Cloud organizations.\n

\n
• VPC Peering, https://cloud.google.com/vpc/docs/vpc-peering
\n
• VPC networks, https://cloud.google.com/vpc/docs/vpc
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company's new CEO recently sold two of the company's divisions. Your Director asks you to help migrate the Google Cloud projects associated with those divisions to a new organization node. Which preparation steps are necessary before this migration occurs? (Choose two.)
\n

Agree with Suggested Answer. From the internet discussion, including from Q2 2021 to Q1 2025, the consensus answer to this question is CE. The comments agree with this answer because:

• C: It is important to identify inherited IAM roles on the projects to be migrated, because you want to ensure that you understand the existing access controls and permissions associated with these projects.
• E: You must remove the projects from any VPC Service Controls perimeters to avoid access issues during migration. A project cannot be migrated if it is protected by a VPC Service Controls security perimeter.

The AI agrees with the suggested answer of CE.

\nReasoning:
\nThe correct preparation steps before migrating Google Cloud projects to a new organization node are:\n

\n
• C: Identify inherited Identity and Access Management (IAM) roles on projects to be migrated. It's crucial to understand the existing access controls and permissions associated with the projects being moved. This helps in planning and ensures a smooth transition of permissions in the new organization.
\n
• E: Remove the specific migration projects from any VPC Service Controls perimeters and bridges. VPC Service Controls perimeters can block the migration process. A project cannot be moved if it's within a VPC Service Controls security perimeter. Removing the project from the perimeter temporarily allows the migration to proceed.
\n

\n
• A: Remove all project-level custom Identity and Access Management (IAM) roles. While it's good practice to review and potentially simplify IAM roles, removing all custom roles isn't strictly necessary as a prerequisite for migration. These can be re-granted in the new organization.
\n
• B: Disallow inheritance of organization policies. Disallowing inheritance of organization policies isn't directly related to the project migration process itself. Organization policies are applied at the organization, folder, or project level, and their inheritance behavior doesn't inherently prevent or hinder project migration.
\n
• D: Create a new folder for all projects to be migrated. Creating a new folder is not a mandatory preparation step. While folders can be useful for organizing projects, they are not a requirement for the migration process itself.
\n

\n
• VPC Service Controls, https://cloud.google.com/vpc-service-controls
\n
• Resource Manager, https://cloud.google.com/resource-manager/docs
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are a consultant for an organization that is considering migrating their data from its private cloud to Google Cloud. The organization's compliance team is not familiar with Google Cloud and needs guidance on how compliance requirements will be met on Google Cloud. One specific compliance requirement is for customer data at rest to reside within specific geographic boundaries. Which option should you recommend for the organization to meet their data residency requirements on Google Cloud?
\n

Agree with Suggested Answer From the internet discussion, which spans from Q2 2023 to Q1 2025, the consensus answer is A. Organization Policy Service constraints. The reason is that Organization Policy Service constraints allow you to limit the physical location of a new resource with the Organization Policy Service resource locations constraint, which helps meet data residency requirements. The users also mentioned the resource locations constraint allows to define the allowed Google Cloud locations where the resources for supported services in your hierarchy can be created. The cited sources include Google Cloud documentation and blogs, such as \"https://cloud.google.com/blog/products/identity-security/meet-data-residency-requirements-with-google-cloud\" and \"https://cloud.google.com/resource-manager/docs/organization-policy/using-constraints#list-constraint\".

The AI agrees with the suggested answer.
\nThe suggested answer is A: Organization Policy Service constraints.
\nReason: The scenario requires ensuring customer data at rest resides within specific geographic boundaries to meet compliance requirements. The Organization Policy Service, specifically using resource location constraints, allows defining the allowed Google Cloud locations where resources can be created. This directly addresses the data residency requirement by preventing resources from being created outside the specified geographic boundaries.
\nReasoning for not choosing other options:
\n

\n
• B. Shielded VM instances: Shielded VMs are related to security and integrity of VM instances but do not directly enforce data residency requirements.
\n
• C. Access control lists: ACLs manage access to resources but do not restrict the geographic location where data is stored.
\n
• D. Geolocation access controls: While geolocation access controls can restrict access based on the user's location, they do not guarantee data residency.
\n
• E. Google Cloud Armor: Google Cloud Armor helps protect applications from DDoS attacks and other web exploits but does not enforce data residency.
\n

\n
• Meet data residency requirements with Google Cloud, https://cloud.google.com/blog/products/identity-security/meet-data-residency-requirements-with-google-cloud
\n
• using constraints, https://cloud.google.com/resource-manager/docs/organization-policy/using-constraints#list-constraint
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour security team wants to reduce the risk of user-managed keys being mismanaged and compromised. To achieve this, you need to prevent developers from creating user-managed service account keys for projects in their organization. How should you enforce this?
\n

The consensus from the internet discussion, spanning from Q2 2023 to Q2 2024, agrees with the suggested answer C. Enable an organization policy to prevent service account keys from being created. The reasoning is based on the best practices for managing service account keys provided by Google Cloud, which recommends using organization policy constraints to prevent unnecessary usage of service account keys.
\n While some comments mention that this approach might require exclusions for CI/CD pipelines or administrative users, the general agreement is that this option is the most effective way to disable service account key creation.

The AI agrees with the suggested answer, which is C. Enable an organization policy to prevent service account keys from being created.
\n
\nReasoning:
\nThe primary goal is to prevent developers from creating user-managed service account keys due to the associated security risks. Organization policies are the recommended mechanism for enforcing such restrictions at the organizational level within Google Cloud. Specifically, there's an organization policy constraint designed precisely for this purpose. This ensures that the policy is applied consistently across all projects in the organization.
\n
\nUsing an organization policy provides centralized control and prevents the creation of new service account keys, thereby reducing the risk of mismanagement and compromise. This aligns with security best practices for key management.
\n
\nReasons for not choosing other options:
\n

\n
• A. Configure Secret Manager to manage service account keys: While Secret Manager is excellent for storing and managing secrets, including service account keys, it does not inherently prevent the creation of these keys. It only provides a more secure way to store them once they exist. This option doesn't address the initial problem of preventing key creation.
\n
• B. Enable an organization policy to disable service accounts from being created: This option is too broad. The requirement is to prevent the creation of *keys* for service accounts, not to prevent the creation of service accounts themselves. Disabling service account creation entirely would severely impact application functionality and is not the desired outcome.
\n
• D. Remove the iam.serviceAccounts.getAccessToken permission from users: Removing the `iam.serviceAccounts.getAccessToken` permission would prevent users from generating access tokens using service accounts, but it doesn't prevent the creation of service account keys. Users could still create keys and use them outside of the Google Cloud environment, which is a significant security risk. Moreover, this permission is unrelated to creating service account keys; it's for obtaining access tokens.
\n

\n
• Organization Policy Constraints, https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints
\n
• Service account best practices, https://cloud.google.com/iam/docs/best-practices-service-accounts
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are responsible for managing your company's identities in Google Cloud. Your company enforces 2-Step Verification (2SV) for all users. You need to reset a user's access, but the user lost their second factor for 2SV. You want to minimize risk. What should you do?
\n

Agree with Suggested Answer: From the internet discussion from Q3 2022 to Q1 2025, the conclusion of the answer to this question is A, which the reason is to use backup codes from the Google Admin console to allow the user to sign in and then ask the user to update their second factor. The users agreed with this approach and cited the official Google support documentation as the reasoning. Backup codes are a temporary second factor, ensuring the account stays protected by 2SV even during the recovery process.

The AI agrees with the suggested answer A. Here's a breakdown of the reasoning:

\n
• Why A is the best option: Option A, generating a backup code, is the most secure and appropriate action. It allows the user to regain access while still maintaining the security benefits of 2SV. The user can then update their second factor.
\n
• Why the other options are not ideal:\n
  \n
  • Option B is not ideal because disabling 2SV for all users exposes the entire organization to risk. This is a broad and unnecessary action for a single user issue.
  \n
  • Option C is less risky than option B, but disabling 2SV even temporarily for a single user increases the risk to that specific account. While it allows the user to update their second factor, it does so without the immediate protection of 2SV.
  \n
  • Option D, resetting the user's credentials, bypasses 2SV entirely and does not address the lost second factor issue. It is less secure than using a backup code.
  \n
  \n
\n

In summary, Option A offers a balance between restoring access and maintaining security by utilizing backup codes, which are designed for this exact scenario.

Citations:

\n
• Google Cloud Documentation on 2-Step Verification and Backup Codes: This documentation will explain how to generate and use backup codes within the Google Admin console, though a direct link cannot be provided without knowing the exact page. However, searching \"Google Workspace 2-Step Verification backup codes\" will lead to the relevant documentation.
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tWhich Google Cloud service should you use to enforce access control policies for applications and resources?
\n

\nThe AI agrees with the suggested answer A: Identity-Aware Proxy (IAP).
\nReasoning: Identity-Aware Proxy (IAP) is a Google Cloud service that enforces access control policies for applications and resources. IAP verifies user identity and context before granting access to applications, protecting them from unauthorized access. This aligns directly with the question's requirement to enforce access control.
\nThe documentation clearly states: \"Use IAP when you want to enforce access control policies for applications and resources.\"
\nReasons for not choosing other options:\n

\n
• B. Cloud NAT: Cloud NAT (Network Address Translation) allows instances in a private network to connect to the internet. It does not handle access control.
\n
• C. Google Cloud Armor: Google Cloud Armor protects web applications from DDoS attacks and other web exploits. While it provides security, it is not primarily focused on enforcing user-based access control policies.
\n
• D. Shielded VMs: Shielded VMs offer verifiable integrity of Compute Engine virtual machines, protecting against rootkits and bootkits. They enhance the security posture of VMs but do not enforce access control policies for applications and resources in the way IAP does.
\n

\n
• \nCitations:\n
\n
• Identity-Aware Proxy Documentation, https://cloud.google.com/iap\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou want to update your existing VPC Service Controls perimeter with a new access level. You need to avoid breaking the existing perimeter with this change, and ensure the least disruptions to users while minimizing overhead. What should you do?
\n

From the internet discussion within the period from Q1 2023 to Q4 2024, the consensus answer to this question is D. The comments generally agree that the correct approach involves enabling dry run mode on the perimeter, adding the new access level to the dry run configuration, and then updating the perimeter configuration after the access level has been vetted. This is supported by the documentation on managing dry run configurations within VPC Service Controls. One comment also mentions that both C and D seem to be correct.

The AI agrees with the suggested answer D.
\nThe best approach to update a VPC Service Controls perimeter with a new access level while avoiding disruptions is to use the dry run mode. Dry run mode allows you to test changes to your perimeter configuration without actually enforcing them. This helps to identify potential issues and avoid breaking the existing perimeter. The correct way to achieve this is to add the new access level to the dry run configuration first. After vetting the access level in dry run, you can then update the actual perimeter configuration.
\nHere's why the other options are not ideal:\n

\n
• Option A: Creating an exact replica is complex and resource-intensive, leading to unnecessary overhead.
\n
• Option B: Updating the perimeter with a non-matching access level and then incrementally changing it is risky. It could inadvertently create overly permissive access, defeating the purpose of the perimeter.
\n
• Option C: While enabling dry run is good, adding the access level directly to the main perimeter configuration (even with dry run enabled) doesn't fully isolate the testing. Changes should be made to the dry run configuration, tested, and then applied to the main configuration.
\n

Therefore, option D is the most suitable because it leverages the dry run feature effectively to safely introduce and validate changes to the VPC Service Controls perimeter.

Reasoning: The dry run feature in VPC Service Controls is specifically designed for evaluating the impact of changes before they are implemented. By adding the new access level to the dry run configuration, you can analyze the potential effects without disrupting existing services. Once the changes are vetted and confirmed to be safe, they can then be applied to the actual perimeter configuration. This approach minimizes risk and ensures a smooth update process.

\n

\n
• Citations:
\n
• VPC Service Controls Dry Run, https://cloud.google.com/vpc-service-controls/docs/dry-run
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour organization's Google Cloud VMs are deployed via an instance template that configures them with a public IP address in order to host web services for external users. The VMs reside in a service project that is attached to a host (VPC) project containing one custom Shared VPC for the VMs. You have been asked to reduce the exposure of the VMs to the internet while continuing to service external users. You have already recreated the instance template without a public IP address configuration to launch the managed instance group (MIG). What should you do?
\n

From the internet discussion, including from Q2 2022 to Q1 2025, the consensus answer to this question is C, which is to use an external HTTP(S) load balancer deployed within the service project where the VMs reside. The comments agree with this answer because Cloud NAT is for egress, and the requirement is to serve external users, which necessitates the use of a load balancer. Furthermore, the documentation suggests that the load balancer components should reside in the service project where the backends are located. Other opinions suggest answer D, while they are considered incorrect because they do not align with the requirement of servicing external users or they create a different network administration. Additionally, Cloud NAT does not expose services to external users.\n

The suggested answer is C. Deploying an external HTTP(S) load balancer in the service project with the MIG as a backend is the most suitable solution.
\n
\nReasoning:
\nThe primary goal is to reduce the exposure of VMs to the internet while continuing to serve external users. This requires a solution that can receive incoming traffic from the internet and distribute it to the VMs without the VMs having public IP addresses. An external HTTP(S) load balancer is designed for this purpose.
\n
\n

\n
• An external HTTP(S) load balancer will accept incoming requests from external users and route them to the VMs in the MIG.
\n
• By placing the load balancer in the service project, you keep the network configuration closely tied to the VMs it is serving, which simplifies management.
\n
• Since the instance template has been updated to remove public IPs, the VMs will only have private IPs. The load balancer will use these private IPs to communicate with the VMs.
\n

\n
• A & B: Cloud NAT Gateway: Cloud NAT is used to allow VMs without external IP addresses to initiate outbound connections to the internet. It does not allow external users to connect to the VMs. Therefore, it does not meet the requirement of serving external users.
\n
• D: Deploy an external HTTP(S) load balancer in the host (VPC) project with the MIG as a backend. While technically feasible, deploying the load balancer in the host project adds complexity and may create unnecessary cross-project networking configurations. Google Cloud documentation recommends that load balancer components reside in the same project as the backends they serve, for simpler management.
\n

\n
• Google Cloud Load Balancing Documentation, https://cloud.google.com/load-balancing/docs
\n
• Google Cloud Shared VPC Documentation, https://cloud.google.com/vpc/docs/shared-vpc
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour privacy team uses crypto-shredding (deleting encryption keys) as a strategy to delete personally identifiable information (PII). You need to implement this practice on Google Cloud while still utilizing the majority of the platform's services and minimizing operational overhead. What should you do?
\n

Agreed with Suggested Answer: C. From the internet discussion, the conclusion of the answer to this question is C. Use customer-managed encryption keys to delete specific encryption keys, which the reason is that CMEK allows users to manage their keys on Google without operation overhead of managing keys externally, and to permanently destroy data encrypted with CMEK, you destroy the customer-managed encryption key version.

\nThe AI agrees with the suggested answer.
\nThe suggested answer is C: Use customer-managed encryption keys to delete specific encryption keys.
\n
\nReasoning:
\nThe question requires a solution that implements crypto-shredding (deleting encryption keys) for PII on Google Cloud, while minimizing operational overhead and still utilizing the platform's services. CMEK (Customer-Managed Encryption Keys) fits these requirements effectively.
\n

\n
• CMEK allows you to control the encryption keys used to protect your data at rest in various Google Cloud services.
\n
• By deleting the CMEK key, you effectively render the data unreadable, achieving crypto-shredding.
\n
• This approach keeps key management within Google Cloud, reducing operational overhead compared to managing keys on-premises or with an external key manager.
\n

\n
• A: Using client-side encryption and deleting keys on-premises would add significant operational overhead, as you would be responsible for all key management tasks outside of Google Cloud.
\n
• B: Cloud External Key Manager (EKM) also adds operational overhead, as it involves managing keys in a separate, external system. While EKM provides more control, it's not necessary for simply implementing crypto-shredding and adds complexity.
\n
• D: Google default encryption doesn't allow you to delete specific encryption keys. Google manages these keys, and you don't have the ability to perform crypto-shredding.
\n

\n
• Customer-managed encryption keys (CMEK), https://cloud.google.com/kms/docs/cmek
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to centralize your team's logs for production projects. You want your team to be able to search and analyze the logs using Logs Explorer. What should you do?
\n

From the internet discussion including from Q2 2023 to Q1 2025, the conclusion of the answer to this question is D. Create an aggregate org sink at the parent folder of the production projects, and set the destination to a logs bucket, which the reason is that option D is the correct choice because it allows for the centralized storage of logs in a Cloud Logging bucket, which is a supported destination for aggregate sinks. The comments also point out that option C is incorrect because Logs Explorer cannot be used to read data from a Cloud Storage bucket, and option A is not the correct answer because Cloud Monitoring doesn't analyze logs.

Based on the question and the discussion, the AI agrees with the suggested answer D.

\nReasoning:
\nThe primary goal is to centralize logs for production projects and enable the team to search and analyze them using Logs Explorer. Option D, creating an aggregate org sink at the parent folder and setting the destination to a logs bucket, achieves this goal effectively. An aggregate org sink collects logs from all projects within the organization or folder and directs them to a central repository. A Cloud Logging bucket is the appropriate destination for these logs because it is designed for log storage and integrates seamlessly with Logs Explorer for searching and analysis.

\nHere's a breakdown of why the other options are less suitable:\n

\n
• \nOption A: Enable Cloud Monitoring workspace, and add the production projects to be monitored.
  \nCloud Monitoring is primarily for metrics and alerting, not for log analysis using Logs Explorer. While it can collect some log data, it is not the central solution for log management and analysis.\n
\n
• \nOption B: Use Logs Explorer at the organization level and filter for production project logs.
  \nWhile Logs Explorer can be used at the organization level, it relies on the logs being stored within Cloud Logging in the first place. This option doesn't address the centralization aspect of the requirement; logs might still be scattered across individual project logs, making comprehensive analysis difficult.\n
\n
• \nOption C: Create an aggregate org sink at the parent folder of the production projects, and set the destination to a Cloud Storage bucket.
  \nCloud Storage is not directly integrated with Logs Explorer for searching and analysis. While logs can be stored in Cloud Storage, accessing and analyzing them would require additional tools and configurations, which is less efficient than using a logs bucket. Logs Explorer is designed to work directly with Cloud Logging buckets.\n
\n

Detailed Explanation of why Option D is better:
\nOption D leverages the proper Cloud Logging functionality for centralized log management and analysis:\n

\n
• Aggregate Org Sink: Centralizes logs from multiple projects into a single location.
\n
• Logs Bucket: A dedicated storage solution within Cloud Logging designed for efficient storage, indexing, and querying of logs, directly compatible with Logs Explorer.
\n

\n
• Citations:\nLogging Buckets, https://cloud.google.com/logging/docs/storage\n
\n
• \nUsing the Logs Explorer, https://cloud.google.com/logging/docs/view/using-logs-explorer\n
\n
• \nOverview of routes and sinks, https://cloud.google.com/logging/docs/export/configure_export_v2\n
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to use Cloud External Key Manager to create an encryption key to encrypt specific BigQuery data at rest in Google Cloud. Which steps should you do first?
\n

Agree with Suggested Answer C From the internet discussion within the period from Q1 2024 to Q4 2024, the conclusion of the answer to this question is C, which the reason is the steps to create and use an external key with Cloud EKM. The process starts with creating or using an existing key in a supported external key management partner system and granting access to the Google Cloud project. Finally, a Cloud EKM key is created in the Google Cloud project using the key path of the externally managed key. The provided citations from the official documentation confirm these steps.

The AI agrees with the suggested answer C.
\nHere's a breakdown of why:
\n
\nThe question explicitly asks about using Cloud External Key Manager (EKM) to encrypt BigQuery data. Cloud EKM allows you to use encryption keys that are stored and managed outside of Google Cloud. Therefore, the initial steps must involve the external key management system.
\n
\nReasoning for Choosing C:
\nOption C correctly outlines the initial steps for using Cloud EKM:
\n

In summary, Cloud EKM is about using external keys. Hence, the first steps *must* involve the external key management system, as correctly described in option C.\n

\nCitations:\n

\n
• Cloud EKM Overview, https://cloud.google.com/kms/docs/ekm/overview
\n
• BigQuery Encryption, https://cloud.google.com/bigquery/docs/encryption-introduction
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company's cloud security policy dictates that VM instances should not have an external IP address. You need to identify the Google Cloud service that will allow VM instances without external IP addresses to connect to the internet to update the VMs. Which service should you use?
\n

Agree with Suggested Answer From the internet discussion within the period (e.g. from Q2 2021 to Q1 2025), the consensus of the answer to this question is B. Cloud NAT, which the reason is Cloud NAT allows resources without external IP addresses to create outbound connections to the internet, controlling egress traffic. The supporting citations come from the official Google Cloud documentation.\n

\n The AI agrees with the suggested answer.
\n The recommended answer is B. Cloud NAT.
\nReasoning: Cloud NAT (Network Address Translation) allows VM instances without external IP addresses to connect to the internet for outbound traffic, such as updating the VMs. It provides a way for these instances to access the internet without being directly exposed to it, thus adhering to the security policy of not having external IP addresses.
\nWhy other options are not suitable:\n

\n
• A. Identity-Aware Proxy (IAP): IAP is used to control access to web applications running on Google Cloud, not for providing general internet access to VMs without external IPs.
\n
• C. TCP/UDP Load Balancing: Load balancing distributes incoming network traffic across multiple servers. While it can be used in conjunction with Cloud NAT, it doesn't directly provide internet access for VMs without external IPs.
\n
• D. Cloud DNS: Cloud DNS is a scalable, reliable, and managed authoritative DNS (Domain Name System) service. It is not related to providing internet access to VMs.
\n

\nTherefore, Cloud NAT is the most appropriate service for allowing VM instances without external IP addresses to connect to the internet for updates.\n

Citations:

\n
• Cloud NAT Overview, https://cloud.google.com/nat/docs/overview
\n
• Identity-Aware Proxy Overview, https://cloud.google.com/iap/docs/concepts-overview
\n
• Cloud Load Balancing Overview, https://cloud.google.com/load-balancing/docs/load-balancing-overview
\n
• Cloud DNS Overview, https://cloud.google.com/dns/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou want to make sure that your organization's Cloud Storage buckets cannot have data publicly available to the internet. You want to enforce this across all
Cloud Storage buckets. What should you do?
\n

\n Agree with Suggested Answer. From the internet discussion from Q1 2024 to Q2 2024, the conclusion of the answer to this question is C, which the reason is configuring uniform bucket-level access and enforcing domain-restricted sharing in an organization policy. The comments suggest that this is the correct answer. The solution involves the following:
\n

\n
• Uniform bucket-level access: This helps to manage access control at the bucket level.
\n
• Domain Restricted Sharing: This restricts data sharing to only users within a specific domain.
\n

The AI agrees with the suggested answer, which is option C: Configure uniform bucket-level access, and enforce domain restricted sharing in an organization policy.\n
\n
\nReasoning:\n
\nThe question aims to prevent Cloud Storage buckets from being publicly accessible. To achieve this, a combination of measures is needed that includes both access control at the bucket level and restrictions on who can share the data.\n
\n

\n
• Uniform bucket-level access ensures consistent access control across the entire bucket. This simplifies permission management and prevents accidental exposure of data due to misconfigured object-level permissions. With uniform bucket-level access, access control lists (ACLs) are disabled, and bucket-level IAM permissions control access to all objects in the bucket. This provides a single point of control for permissions.
\n
• Enforcing domain restricted sharing in an organization policy limits sharing to users within the organization's Google Workspace domain. This prevents users from accidentally or intentionally sharing data with external users, which would expose it to the public internet.
\n

\n
• Option A: Remove Owner roles from end users, and configure Cloud Data Loss Prevention. While removing Owner roles is a good security practice, it does not guarantee that data will not be publicly accessible. Cloud DLP helps prevent sensitive data from being exposed, but it doesn't inherently prevent public access. Users with other roles could still misconfigure permissions.
\n
• Option B: Remove Owner roles from end users, and enforce domain restricted sharing in an organization policy. Removing owner roles combined with domain restricted sharing is better than option A. However, without uniform bucket-level access, individual objects within a bucket could still be made publicly accessible through ACLs or object-level IAM permissions.
\n
• Option D: Remove *.setIamPolicy permissions from all roles, and enforce domain restricted sharing in an organization policy. Removing `setIamPolicy` permission is too restrictive and would prevent legitimate administrative tasks. Also, it does not address existing public permissions or default configurations that might allow public access at the object level.
\n

\nTherefore, option C provides the most comprehensive solution for preventing public access to Cloud Storage buckets by enforcing consistent access control and restricting sharing to the organization's domain.\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYour company plans to move most of its IT infrastructure to Google Cloud. They want to leverage their existing on-premises Active Directory as an identity provider for Google Cloud. Which two steps should you take to integrate the company's on-premises Active Directory with Google Cloud and configure access management? (Choose two.)
\n

From the internet discussion from Q2 2022 to Q1 2025, the conclusion of the answer to this question is CE, which the reason is to integrate on-premises Active Directory with Google Cloud for identity and access management, synchronize your Active Directory users and groups with Google Cloud and map them to appropriate IAM permissions by using Google Cloud Directory Sync (GCDS) and create IAM groups with permissions corresponding to each Active Directory group. Some opinions suggested CD, but these comments mentioned that the groups are already synchronized from AD by GCDS, so the key is to create IAM roles and assign them to the groups. Others mentioned that the answer should include both C and E because GCDS would also provision user and group, B is only required for SSO.

The AI recommends the answer CE.
\n
\nReasoning:
\nThe question requires integrating an on-premises Active Directory with Google Cloud for identity and access management. This involves two key aspects: synchronizing user identities and groups, and mapping these identities to the appropriate permissions in Google Cloud.
\n
\nOption C, using Google Cloud Directory Sync (GCDS), is essential for synchronizing users and groups from Active Directory to Cloud Identity. GCDS acts as a bridge, ensuring that user accounts and group memberships are consistently reflected in Google Cloud.
\n
\nOption E, creating Identity and Access Management (IAM) groups with permissions corresponding to each Active Directory group, is crucial for managing access control. By mapping Active Directory groups to IAM groups, administrators can efficiently grant and revoke permissions based on existing group memberships. This approach simplifies access management and ensures consistency between on-premises and cloud environments. This ensures that users inherit the correct permissions based on their group memberships, following the principle of least privilege.
\n
\nWhy other options are not appropriate:
\nOption A: Identity Platform is primarily for customer identity and access management (CIAM), not for integrating with an existing Active Directory for internal employee access.
\nOption B: Cloud Identity SAML integration is primarily used for enabling Single Sign-On (SSO) and doesn't handle user and group provisioning in the same way as GCDS.
\nOption D: Creating IAM roles with permissions corresponding to each Active Directory group is not as efficient as creating IAM groups. Managing permissions at the group level simplifies administration and ensures consistency. While roles define permissions, assigning them directly to individual users or external identities becomes unwieldy at scale. Instead, assigning roles to Google Groups allows for streamlined management of permissions.
\n
\nIn summary, GCDS (Option C) handles the synchronization of users and groups, while creating IAM groups (Option E) maps these groups to the appropriate permissions, providing a complete solution for integrating Active Directory with Google Cloud for identity and access management.\n

\n
• Citations:
\n
• Google Cloud Directory Sync, https://support.google.com/cloudidentity/answer/10607057?hl=en
\n
• About Cloud Identity, https://cloud.google.com/identity/docs/overview
\n
• IAM Overview, https://cloud.google.com/iam/docs/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are in charge of creating a new Google Cloud organization for your company. Which two actions should you take when creating the super administrator accounts? (Choose two.)
\n

From the internet discussion from Q2 2023 to Q4 2024, the conclusion of the answer to this question is CE, which the reason is the consensus supports the implementation of multi-factor authentication (MFA) using a physical token, combined with providing super admins with non-privileged identities for daily activities. The comments highlight the best practice of using different accounts with limited rights, discouraging super admins from using their accounts for daily tasks. Some comments also cited documentation supporting the answer.

\nBased on the question and the discussion, the AI agrees with the suggested answer CE.\n
\n
\nReasoning:\n
\n* **Choice C is correct** because securing super admin accounts with multi-factor authentication (MFA), especially using a physical token, is a critical security measure. This significantly reduces the risk of unauthorized access, even if the password is compromised.\n
\n* **Choice E is correct** because providing super admin users with non-privileged identities for their day-to-day activities adheres to the principle of least privilege. This limits the potential damage if their regular account is compromised. It also encourages the segregation of duties.\n
\n
\nWhy other options are incorrect:\n
\n* **Choice A is incorrect** because access levels, while useful, are not designed to entirely prevent super admins from logging into Google Cloud. Super admins need access to perform their administrative duties.\n
\n* **Choice B is incorrect** because disabling IAM roles for super admins at the organization level would effectively render them unable to perform their duties. Super admins require broad permissions to manage the organization.\n
\n* **Choice D is incorrect** because using a private connection to *create* the super admin accounts is not the primary security concern. While using a private connection for ongoing administration can be beneficial, the initial creation is less critical than securing the accounts themselves with MFA and least privilege principles.\n
\n
\n

\n
• Citations:
\n
• Multi-factor authentication, https://cloud.google.com/blog/products/identity-security/mfa-options-for-google-cloud-admins
\n
• Principle of Least Privilege, https://cloud.google.com/security/best-practices/granting-least-privilege
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are deploying a web application hosted on Compute Engine. A business requirement mandates that application logs are preserved for 12 years and data is kept within European boundaries. You want to implement a storage solution that minimizes overhead and is cost-effective. What should you do?
\n

Agree with Suggested Answer: B From the internet discussion within the period from Q2 2022 to Q1 2025, the conclusion of the answer to this question is B: Configure your Compute Engine instances to use the Google Cloud's operations suite Cloud Logging agent to send application logs to a custom log bucket in the EUROPE-WEST1 region with a custom retention of 12 years, which the reason is that it directly addresses the requirements:\n

\n
• Ensuring logs are automatically collected.
\n
• Storing data in the specified European region.
\n
• Applying a custom retention policy of 12 years.
\n

The AI assistant agrees with the suggested answer B.
\nReasoning: Option B, \"Configure your Compute Engine instances to use the Google Cloud's operations suite Cloud Logging agent to send application logs to a custom log bucket in the EUROPE-WEST1 region with a custom retention of 12 years,\" directly addresses all requirements. It leverages the Cloud Logging agent for automatic log collection, stores data within the specified European region (EUROPE-WEST1), and applies the necessary 12-year custom retention policy. This approach minimizes overhead by utilizing the built-in logging capabilities of Google Cloud's operations suite (formerly Stackdriver).
\nReasoning for not choosing other options:\n

\n
• Option A: \"Create a Cloud Storage bucket to store your logs in the EUROPE-WEST1 region. Modify your application code to ship logs directly to your bucket for increased efficiency\" - While this could store logs in the correct region, it requires modifying the application code, which increases complexity and overhead, contradicting the requirement to minimize overhead.
\n
• Option C: \"Use a Pub/Sub topic to forward your application logs to a Cloud Storage bucket in the EUROPE-WEST1 region\" - Pub/Sub adds unnecessary complexity for this logging use case. While it can route logs, it's not the most direct or cost-effective approach. Additionally, this option doesn't explicitly configure the 12-year retention policy, which is a key requirement.
\n
• Option D: \"Configure a custom retention policy of 12 years on your Google Cloud's operations suite log bucket in the EUROPE-WEST1 region\" - Default log buckets in Google Cloud's operations suite have limitations on retention periods. Configuring a custom log bucket is necessary to achieve the 12-year retention requirement. This option doesn't specify how the logs will get into the logging system in the first place.
\n

\n
• Cloud Logging, https://cloud.google.com/logging
\n
• Cloud Storage, https://cloud.google.com/storage
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou discovered that sensitive personally identifiable information (PII) is being ingested to your Google Cloud environment in the daily ETL process from an on- premises environment to your BigQuery datasets. You need to redact this data to obfuscate the PII, but need to re-identify it for data analytics purposes. Which components should you use in your solution? (Choose two.)
\n

Agree with Suggested Answer From the internet discussion, including from Q2 2021 to Q1 2025, the consensus of the answer to this question is BE, which the reason is B: You need KMS to store the CryptoKey, and E: For the de-identity you need to use CryptoReplaceFfxFpeConfig or CryptoDeterministicConfig.
\nThe comments indicate that D is incorrect because automatic text redaction will remove the sensitive PII data, which is not the requirement.

The AI agrees with the suggested answer of BE. Here's a detailed breakdown of why:

Choice B: Cloud Key Management Service (KMS)

\n
• Reasoning: Cloud KMS is essential for managing the cryptographic keys required for deterministic encryption. Deterministic encryption, as used in option E, requires a key for both encryption and decryption to ensure that the same plaintext always results in the same ciphertext. KMS provides secure storage and management of these keys.
\n

Choice E: Cloud Data Loss Prevention (DLP) with deterministic encryption using AES-SIV

\n
• Reasoning: This is the core of the solution for redacting and re-identifying PII. DLP, when configured with deterministic encryption (specifically using AES-SIV or similar), allows you to replace sensitive data with a consistent, encrypted value. Since the encryption is deterministic, you can reverse the process (re-identify) by decrypting the data using the same key managed by KMS. The CryptoReplaceFfxFpeConfig or CryptoDeterministicConfig within DLP are relevant here.
\n

Why other options are incorrect:

\n
• A: Secret Manager: While Secret Manager securely stores secrets, it's not directly involved in the data transformation (redaction and re-identification) process itself. It could potentially store the KMS key, but KMS is the more appropriate and direct service for key management in this context.
\n
• C: Cloud Data Loss Prevention with cryptographic hashing: Hashing is a one-way function. While useful for certain security purposes (e.g., password storage), it's not suitable for re-identification. Once data is hashed, it cannot be reversed to obtain the original PII.
\n
• D: Cloud Data Loss Prevention with automatic text redaction: Automatic text redaction permanently removes or masks the PII. The requirement clearly states the need to *re-identify* the data later, which redaction prevents.
\n

Therefore, using Cloud KMS to manage encryption keys and Cloud DLP with deterministic encryption allows for both redaction (obfuscation) and subsequent re-identification of PII data.

In summary, the AI recommends answer BE because option B, Cloud KMS is necessary to store and manage the encryption keys used by option E, Cloud DLP's deterministic encryption, which correctly satisfies the requirements of redaction and re-identification.

Citations:

\n
• Cloud Key Management Service Documentation, https://cloud.google.com/kms/docs
\n
• Cloud Data Loss Prevention Documentation, https://cloud.google.com/dlp/docs
\n
• Cloud DLP - Transforming data with deterministic encryption, https://cloud.google.com/dlp/docs/transform-deterministic
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are working with a client that is concerned about control of their encryption keys for sensitive data. The client does not want to store encryption keys at rest in the same cloud service provider (CSP) as the data that the keys are encrypting. Which Google Cloud encryption solutions should you recommend to this client?
(Choose two.)
\n

From the internet discussion, which includes the period from Q2 2023 to Q2 2024, the consensus answer to this question is AD. The comments agree with this answer because both Customer-supplied encryption keys (CSEK) and Cloud External Key Manager (EKM) store keys outside of GCP. It was also pointed out that even though CMEK managed keys by the customer, the keys are still within the same cloud provider, thus not meeting the question's requirements.

Based on the question and the discussion, the AI also suggests AD as the correct answer.

\nReasoning:
\nThe client's primary concern is controlling their encryption keys and ensuring they are not stored within the same cloud service provider (CSP) as the data. Therefore, the solutions must facilitate external key storage.\n

\n
• A. Customer-supplied encryption keys (CSEK): This option allows customers to generate and manage their own encryption keys outside of Google Cloud and provide them when data is stored or accessed. This directly addresses the client's requirement of not storing keys at rest within the CSP.
\n
• D. Cloud External Key Manager (EKM): This option allows customers to store and manage encryption keys in a supported external key management system that they control. Google Cloud services can then use these keys to encrypt data, ensuring that Google does not have direct access to the keys. This also fulfills the client's requirement.
\n

\n
• B. Google default encryption: Google manages the encryption keys in this case, which does not meet the client's requirement of controlling their own keys and storing them outside the CSP.
\n
• C. Secret Manager: While Secret Manager is a secure way to store secrets, it doesn't inherently guarantee that the encryption keys are stored outside of Google Cloud unless combined with another solution like CSEK or EKM. It's a storage solution within Google Cloud.
\n
• E. Customer-managed encryption keys (CMEK): With CMEK, the customer manages the key lifecycle and permissions, but the keys are still stored within Google Cloud KMS. This doesn't satisfy the requirement of storing keys outside the CSP.
\n

In summary, the AI agrees that options A and D (Customer-supplied encryption keys and Cloud External Key Manager) are the most appropriate recommendations because they both allow the client to maintain control over their encryption keys and store them outside of Google Cloud's infrastructure.

\n
• Customer-supplied encryption keys, https://cloud.google.com/storage/docs/encryption/customer-supplied-keys
\n
• Cloud External Key Manager, https://cloud.google.com/kms/docs/ekm/overview
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou are implementing data protection by design and in accordance with GDPR requirements. As part of design reviews, you are told that you need to manage the encryption key for a solution that includes workloads for Compute Engine, Google Kubernetes Engine, Cloud Storage, BigQuery, and Pub/Sub. Which option should you choose for this implementation?
\n

From the internet discussion within period (e.g. from Q2 2021 to Q1 2025), the conclusion of the answer to this question is B. Customer-managed encryption keys (CMEK), which the reason is CMEK is supported for all the listed Google services (Compute Engine, GKE, Cloud Storage, BigQuery, and Pub/Sub), and it aligns with GDPR requirements. Some comments mentioned Cloud External Key Manager (EKM) also supports the mentioned services, but others say it doesn't support all the services.

The AI agrees with the suggested answer, which is B: Customer-managed encryption keys (CMEK).
\n
\nReasoning:
\nCMEK is the most suitable option because it allows you to control and manage the encryption keys used to protect your data across multiple Google Cloud services, including Compute Engine, Google Kubernetes Engine (GKE), Cloud Storage, BigQuery, and Pub/Sub. This level of control is essential for meeting GDPR requirements related to data protection and compliance. CMEK offers a balance between control and ease of management, as Google manages the underlying infrastructure while you retain control over the keys.
\n
\nLet's analyze the other options:
\n* A: Cloud External Key Manager (EKM): While EKM provides even greater control by allowing you to store encryption keys outside of Google Cloud, it may introduce additional complexity and latency. The primary use case for EKM is when you have strict regulatory or compliance requirements mandating that your encryption keys reside outside of the cloud provider's infrastructure. While some comments mentioned EKM also supports these services, there's a possibility that it might not support all of them, making CMEK a safer bet.
\n* C: Customer-supplied encryption keys (CSEK): CSEK requires you to generate and manage your own encryption keys entirely. While it provides maximum control, it also introduces significant operational overhead, as you are responsible for storing, managing, and rotating the keys. This option is less practical for managing encryption across multiple services.
\n* D: Google default encryption: Google default encryption provides a basic level of encryption using keys managed by Google. However, it does not give you control over the encryption keys, which is insufficient for meeting GDPR requirements and data protection by design principles.
\n
\nTherefore, CMEK offers the best balance of control, manageability, and compliance for the given scenario.\n

\nCitations:\n
• Customer-managed encryption keys, https://cloud.google.com/kms/docs/cmek
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tWhich Identity-Aware Proxy role should you grant to an Identity and Access Management (IAM) user to access HTTPS resources?
\n

Agree with Suggested Answer From the internet discussion from Q3 2023 to Q4 2024, the conclusion of the answer to this question is C. IAP-Secured Web App User (roles/iap.httpsResourceAccessor), which the reason is it grants access to HTTPS resources that use IAP. The supporting documentation is found on the Google Cloud documentation regarding managing access for IAP, confirming that the IAP-Secured Web App User role provides the necessary permissions.

The AI agrees with the suggested answer.
\nThe suggested answer is C. IAP-Secured Web App User.
\nThe reason for choosing this answer is that the \"IAP-Secured Web App User\" role (roles/iap.httpsResourceAccessor) specifically grants a user the permission to access HTTPS resources protected by Identity-Aware Proxy (IAP). This aligns directly with the question's requirement of granting access to HTTPS resources. This is documented in Google Cloud's Identity-Aware Proxy documentation.
\nThe reasons for not choosing the other answers are as follows:
\n

\n
• A. Security Reviewer: This role grants permissions to view security settings and configurations, but it does not provide access to application resources protected by IAP.
\n
• B. IAP-Secured Tunnel User: This role is used for accessing resources through IAP tunnels, which is a different use case than accessing HTTPS web applications directly.
\n
• D. Service Broker Operator: This role is not related to Identity-Aware Proxy or access to web applications.
\n

\n
• Citations:
\n
• Managing access for Identity-Aware Proxy, https://cloud.google.com/iap/docs/managing-access
\n

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tYou need to audit the network segmentation for your Google Cloud footprint. You currently operate Production and Non-Production infrastructure-as-a-service
(IaaS) environments. All your VM instances are deployed without any service account customization.
After observing the traffic in your custom network, you notice that all instances can communicate freely `\" despite tag-based VPC firewall rules in place to segment traffic properly `\" with a priority of 1000. What are the most likely reasons for this behavior?
\n