The best combination of steps to meet the requirements are A and C. Here's a detailed explanation:

Option A: Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.

Reasoning: Redshift Federated Query allows Redshift to directly query data residing in other databases like Aurora PostgreSQL. This enables the data engineer to query live transactional data in PostgreSQL without needing to move it to Redshift. This addresses the requirement to effectively combine data from live transactional data in PostgreSQL with data in Redshift.
Why it's correct: Federated Query is designed for querying external data sources directly from Redshift.

Option C: Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.

Reasoning: This addresses the need to archive historical data and reduce costs in Redshift. By moving data older than 15 months to Amazon S3 and using Redshift Spectrum, the company can query this historical data without keeping it in the more expensive Redshift storage. It also addresses the need to run analytics queries that effectively combine data from all sources.
Why it's correct: UNLOAD is efficient for exporting data from Redshift to S3. Redshift Spectrum is specifically designed to query data in S3.

Let's analyze why the other options are not as suitable:

Option B: Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.
- Reasoning: Redshift Spectrum is designed to query data in Amazon S3, not directly from databases like PostgreSQL.
- Why it's incorrect: Spectrum cannot directly query PostgreSQL.
Option D: Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.
- Reasoning: Redshift Spectrum cannot directly query data stored in Amazon S3 Glacier Flexible Retrieval. Spectrum requires data to be in S3 Standard, S3 Intelligent-Tiering, S3 Standard Infrequent Access (S3 Standard IA), or S3 One Zone IA storage classes.
- Why it's incorrect: Redshift Spectrum does not support querying data in S3 Glacier.
Option E: Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.
- Reasoning: While materialized views can help with query performance, they don't address the cost optimization requirement. Storing all data (live, current, and historical) in Redshift would be more expensive than archiving historical data to S3. Also, materialized views may not be automatically updated with live transactional data, which requires to additional configurations and maintenance.
- Why it's incorrect: It does not help to reduce cost, which is the most important requirement.

Redshift Federated Query, https://docs.aws.amazon.com/redshift/latest/dg/ federated-query.html
Redshift Spectrum, https://docs.aws.amazon.com/redshift/latest/dg/c-using-spectrum.html
Redshift UNLOAD command, https://docs.aws.amazon.com/redshift/latest/dg/r_UNLOAD.html