DEA-C01 Data Operations and Support Practice Question

A data engineer is troubleshooting a Kinesis Data Analytics application that processes streaming data. The application is falling behind and has a high 'MillisBehindLatest' metric. The application uses a parallelism of 2. The source stream has 4 shards. What is the MOST likely cause and solution?

⚠ Common exam trap

It's easy for candidates to assume increasing parallelism always improves performance, but the key insight is that parallelism must match or exceed the number of source shards to avoid a concurrency bottleneck, not just be arbitrarily high.

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

✓

The application parallelism is too low; increase it to 4.

The 'MillisBehindLatest' metric indicates the application is not keeping up with the incoming data. With a source stream of 4 shards and a parallelism of only 2, the application cannot process data from all shards concurrently, leading to backpressure. Increasing parallelism to match the shard count (4) allows each shard to be processed by a separate task, reducing lag.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The application is using a JSON format; switch to Avro.
Why it's wrong here
Data format does not affect processing speed significantly.
✗
The source stream has too many shards; decrease to 2.
Why it's wrong here
Reducing shards decreases throughput capacity, making the problem worse.
✓
The application parallelism is too low; increase it to 4.
Why this is correct
With 4 shards, parallelism should be at least 4 to process all shards concurrently.
✗
The output destination is slow; change to a faster sink.
Why it's wrong here
The high MillisBehindLatest indicates the application is not keeping up with ingestion, not the sink.

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Same concept, more angles

1 more way this is tested on DEA-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A data engineer is troubleshooting a Kinesis Data Analytics application that processes streaming data. The application is falling behind, and the metric 'MillisBehindLatest' is consistently above 60000. The source Kinesis stream has 10 shards, and the application uses a Flink application with default parallelism. What is the MOST likely cause of the lag?

medium

A.The sink (destination) is throttling writes.
✓ B.The Flink application parallelism is set to 1.
C.The Kinesis stream has too few shards.
D.The retention period of the Kinesis stream is too short.

Why B: In Kinesis Data Analytics with Flink, the default parallelism is 1. With 10 shards in the source Kinesis stream, a single parallel task must read from all shards, creating a bottleneck and causing the 'MillisBehindLatest' metric to be consistently high (above 60000). Option A is wrong because while a throttling sink can cause backpressure, the question specifically states the application is falling behind and the metric is high, which is more directly explained by insufficient parallelism. Option C is wrong because 10 shards is typically sufficient; the issue is how they are consumed. Option D is wrong because the retention period does not affect the lag metric; it only controls how long data is stored.

Last reviewed: Jun 24, 2026

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