DEA-C01 Data Ingestion and Transformation Practice Question
A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application reads from a Kinesis stream and writes aggregated results to an Amazon S3 bucket. The company notices that the application is falling behind and the checkpoint duration is increasing. Which THREE actions should the data engineer take to improve performance? (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 multiple S3 prefixes in the output path to avoid throttling.
Options B, D, and E are correct. Using multiple S3 prefixes in the output path (B) reduces the risk of S3 write throttling by distributing writes across multiple partition keys. Increasing the checkpoint interval (D) reduces the frequency of checkpointing, thus decreasing the overhead and allowing the application to process more data between checkpoints. Increasing parallelism (E) allows the Flink application to process more data in parallel, improving throughput. Decreasing the number of shards (A) would reduce the incoming data rate and potentially worsen the lag. Increasing heap memory (C) might help with memory pressure but does not directly address checkpoint duration or processing lag; the primary issues are related to parallelism and checkpoint overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the number of shards in the source Kinesis stream.
Why it's wrong here
Fewer shards reduce throughput, worsening backlog.
- ✓
Use multiple S3 prefixes in the output path to avoid throttling.
Why this is correct
Multiple prefixes increase S3 write performance.
- ✗
Increase the heap memory of the Flink application.
Why it's wrong here
Heap memory helps with state size, not checkpoint duration directly.
- ✓
Increase the checkpoint interval to reduce checkpoint overhead.
Why this is correct
Longer intervals reduce the frequency of checkpoint operations.
- ✓
Increase the parallelism of the Flink application.
Why this is correct
More parallelism can increase throughput.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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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.