DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. They notice that the application's checkpointing is failing intermittently, causing data reprocessing. The application uses a large state. Which configuration change should the team make to improve checkpoint reliability?
⚠ Common exam trap
A common mix-up: candidates confuse improving state backend performance (RocksDB) with fixing checkpoint reliability, when the root cause is checkpoint timing pressure, not state storage efficiency.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the checkpointing interval.
Increasing the checkpointing interval reduces the frequency of checkpoint operations, giving the system more time to complete each checkpoint before the next one starts. This alleviates backpressure and resource contention, which is critical when dealing with large state, as checkpointing large state is I/O and CPU intensive and can fail if intervals are too tight.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable checkpointing to avoid failures.
Why it's wrong here
Disabling checkpointing removes fault tolerance.
- ✗
Switch the state backend from in-memory to RocksDB.
Why it's wrong here
RocksDB helps manage large state but does not directly fix checkpoint failures.
- ✗
Increase the parallelism of the application.
Why it's wrong here
Higher parallelism can increase checkpoint overhead.
- ✓
Increase the checkpointing interval.
Why this is correct
Longer intervals reduce checkpoint frequency and associated failures.
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