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Data Ingestion and TransformationhardMultiple ChoiceObjective-mapped

DEA-C01 Data Ingestion and Transformation Practice Question

Network Topology
$ aws kinesis describe-streamstream-name my-data-streamRefer to the exhibit.```"StreamDescription": {"StreamName": "my-data-stream","StreamARN": "arn:aws:kinesis:us-east-1:123456789012:stream/my-data-stream","StreamStatus": "ACTIVE","Shards": ["ShardId": "shardId-000000000000","ParentShardId": null,"AdjacentParentShardId": null,"HashKeyRange": {"StartingHashKey": "0","EndingHashKey": "170141183460469231731687303715884105727"},"SequenceNumberRange": {"StartingSequenceNumber": "49598123064581000000000000000000000000000000000000000001","EndingSequenceNumber": null"ShardId": "shardId-000000000001","StartingHashKey": "170141183460469231731687303715884105728","EndingHashKey": "340282366920938463463374607431768211455""StartingSequenceNumber": "49598123064581000000000000000000000000000000000000000002",

A data engineer is troubleshooting a Kinesis Data Streams application that is experiencing high latency. The stream has 2 shards. The application is using a single Kinesis Client Library (KCL) worker to process all shards. Which change will MOST likely reduce latency?

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

Deploy multiple KCL workers to process shards in parallel.

The application uses a single KCL worker to process all 2 shards, which processes records sequentially and causes high latency. Deploying multiple KCL workers (ideally one per shard) enables parallel processing of shards, significantly reducing latency. Option A is incorrect because increasing shard count to 4 adds more capacity but does not address the bottleneck of a single worker; the same worker would process all 4 shards sequentially, potentially worsening latency. Option C is incorrect because Kinesis Data Streams is a managed service; there is no instance type to change for the stream itself. The KCL worker runs on your compute resources, not on the stream. Option D is incorrect because decreasing shards to 1 reduces the level of parallelism, increasing the workload per shard and likely increasing latency further.

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 to 4.

    Why it's wrong here

    Increasing shards increases capacity, but if a single worker processes all shards, it may still be a bottleneck.

  • Deploy multiple KCL workers to process shards in parallel.

    Why this is correct

    Multiple workers can process shards concurrently, reducing latency.

  • Use a larger instance type for the Kinesis stream.

    Why it's wrong here

    Kinesis streams are serverless; you cannot choose instance types.

  • Decrease the number of shards to 1.

    Why it's wrong here

    Decreasing shards reduces parallelism and may increase latency.

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Last reviewed: Jun 20, 2026

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