The AI suggests that the suggested answer A is correct.
Reasoning:
Structured Streaming in Apache Spark uses two key mechanisms to ensure fault tolerance and exactly-once processing: Checkpointing and Write-Ahead Logs (WAL).

Checkpointing: Checkpointing involves saving the state of the streaming application to a reliable storage system (e.g., HDFS, cloud storage). This state includes the offset ranges of the data that has been processed, along with any aggregations or other transformations that have been applied. If the application fails, it can restart from the last checkpoint and continue processing from where it left off, avoiding data loss or duplication.
Write-Ahead Logs (WAL): WALs are used to record every transaction or operation before it is applied to the state. This ensures that even if a failure occurs during processing, the operations can be replayed from the WAL to recover the state. In the context of Structured Streaming, the WAL records the offset ranges of the data being processed in each trigger, providing a detailed log of the progress.

Together, checkpointing and WALs enable Structured Streaming to reliably track the progress of processing and handle failures by restarting and/or reprocessing data as needed. This combination ensures that the application can recover from failures without losing data or processing the same data multiple times.
Reasons for not choosing other options:

B. Structured Streaming cannot record the offset range of the data being processed in each trigger: This is incorrect because Structured Streaming explicitly records and manages offsets for fault tolerance.
C. Replayable Sources and Idempotent Sinks: While replayable sources and idempotent sinks contribute to fault tolerance, they don't directly address the recording of offset ranges. Replayable sources can provide data again, and idempotent sinks ensure that writing the same data multiple times has the same effect as writing it once, but these do not inherently track processing progress.
D. Write-ahead Logs and Idempotent Sinks: Write-ahead logs are used, but idempotent sinks are not the mechanism by which Spark records the offset range. Idempotent sinks ensure that the output is consistent even if written multiple times, which is a separate concern from tracking progress.
E. Checkpointing and Idempotent Sinks: Checkpointing is essential, but as with option D, idempotent sinks do not record offset ranges. Checkpointing captures the state including offset ranges, allowing restart from a known point.

Citations:
Apache Spark Documentation, https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html