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Data Operations and SupporthardMultiple SelectObjective-mapped

DEA-C01 Data Operations and Support Practice Question

A company is using Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) for streaming data processing. The application is experiencing high latency and the data engineer wants to improve performance. Which THREE actions should the engineer consider? (Choose three.)

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

Use a larger Kinesis data stream with more shards.

Options A, C, and D are correct. Increasing the number of shards in the Kinesis stream (A) increases the ingestion capacity, which can reduce bottlenecks and lower latency. Increasing Flink parallelism (C and D) allows more parallel processing of data, improving throughput and reducing latency. Option B is incorrect because decreasing the buffer time in Flink can lead to increased network overhead and may not improve overall performance; it can actually increase latency due to more frequent data transfers. Option E is incorrect because decreasing the checkpoint interval increases the frequency of state snapshots, adding overhead and potentially increasing latency rather than reducing it.

Answer analysis

Option-by-option breakdown

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

  • Use a larger Kinesis data stream with more shards.

    Why this is correct

    More shards provide higher throughput.

  • Decrease the buffer time in the Flink application to reduce latency.

    Why it's wrong here

    Smaller buffer time increases micro-batching overhead, potentially increasing latency.

  • Increase the Flink parallelism parameter in the application configuration.

    Why this is correct

    Higher parallelism improves throughput.

  • Increase the Parallelism of the Flink application.

    Why this is correct

    More parallelism allows parallel processing.

  • Decrease the checkpoint interval to reduce state size.

    Why it's wrong here

    Decreasing checkpoint interval increases overhead and latency.

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