Answer: Checkpointing and Write-ahead Logs

**Correct Answer: A (Checkpointing and Write-ahead Logs)** **Explanation:** Structured Streaming uses two key mechanisms to ensure fault tolerance and exactly-once processing semantics: 1. **Checkpointing**: This stores the current state of the streaming query, including the progress of data processing. Checkpoints contain metadata about the query's progress, including the offset ranges that have been processed. 2. **Write-ahead Logs (WAL)**: These logs record the data that is about to be processed before it's actually processed. This ensures that if a failure occurs during processing, the system can recover and reprocess the data from the log. **Why other options are incorrect:** - **B**: Incorrect - Structured Streaming can and does record offset ranges through checkpointing and WAL. - **C**: While replayable sources and idempotent sinks are important concepts in streaming systems, they are not the primary mechanisms Spark uses to track offset ranges for fault tolerance. - **D**: Write-ahead logs are correct, but idempotent sinks alone don't track offset ranges. - **E**: Checkpointing is correct, but idempotent sinks don't track offset ranges; they ensure that duplicate writes don't cause data corruption. **Key Concepts:** - **Offset Range**: The range of data positions (offsets) that have been processed in a streaming source. - **Fault Tolerance**: The ability to recover from failures without data loss or duplication. - **Exactly-once Semantics**: Guaranteeing that each record is processed exactly once, even in the face of failures. This combination allows Structured Streaming to restart from the last known good state and reprocess any data that wasn't successfully written to the sink.

Author: Keng Suppaseth