DEA-C01 Data Operations and Support Practice Question
A data engineer is monitoring an Amazon Kinesis Data Analytics for Apache Flink application that processes streaming data. The application is falling behind (increasing 'MillisBehindLatest') and the CPU utilization of the Flink task managers is consistently above 80%. Which THREE actions should the engineer take to improve performance? (Choose THREE.)
⚠ Common exam trap
Test-takers frequently confuse decreasing checkpoint intervals with improving performance, not realizing that more frequent checkpoints increase CPU and I/O overhead, making the lag worse.
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 number of shards in the Kinesis data stream.
Increasing the number of shards in the Kinesis data stream (Option A) directly increases the ingestion capacity and parallelism source for the Flink application. With more shards, the application can read data from more partitions concurrently, reducing the backlog indicated by 'MillisBehindLatest'. This is a fundamental scaling action for Kinesis-based Flink applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the number of shards in the Kinesis data stream.
Why this is correct
More shards allow higher ingestion rate.
- ✗
Decrease the checkpoint interval to reduce state size.
Why it's wrong here
More frequent checkpoints increase overhead.
- ✓
Enable auto-scaling for the Flink application.
Why this is correct
Auto-scaling adjusts resources based on load.
- ✗
Decrease the number of task managers to reduce CPU contention.
Why it's wrong here
Fewer task managers reduce capacity.
- ✓
Increase the Flink application's parallelism.
Why this is correct
More parallel tasks improve throughput.
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