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Data Ingestion and TransformationhardMultiple ChoiceObjective-mapped

DEA-C01 Data Ingestion and Transformation Practice Question

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis stream with 10 shards and writes to an S3 bucket. The application is experiencing high latency. Analysis shows that the application is not keeping up with the incoming data rate. Which action would MOST effectively reduce latency?

⚠ Common exam trap

Many exam-takers confuse scaling the infrastructure (KPU or shards) with scaling the application logic (parallelism), assuming that more shards or larger instances automatically resolve processing bottlenecks without explicitly tuning the Flink application's parallelism.

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 Parallelism of the Flink application

The high latency is caused by the Flink application not keeping up with the incoming data rate, which indicates a processing bottleneck within the application itself. Increasing the Parallelism of the Flink application (Option B) directly increases the number of parallel subtasks that can process data concurrently, improving throughput and reducing latency. This is the most effective action because it addresses the root cause—insufficient compute resources for stream processing—without changing the source or sink configuration.

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 stream

    Why it's wrong here

    More shards increase input rate; if the application can't keep up, it worsens latency.

  • Increase the Parallelism of the Flink application

    Why this is correct

    Higher parallelism allows more concurrent processing.

  • Enable exactly-once delivery to S3

    Why it's wrong here

    Exactly-once adds overhead and may increase latency.

  • Use a larger Kinesis Data Analytics application (increase KPU)

    Why it's wrong here

    While increasing KPU can help, parallelism is more direct; KPU may be limited by default.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.