DEA-C01 Data Ingestion and Transformation Practice Question
A company is using Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an Amazon S3 bucket. The team notices that the application is experiencing high latency during peak hours. The stream has 8 shards, and the application is configured with a parallelism of 4. Which action would most likely reduce the latency?
⚠ Common exam trap
The DEA-C01 exam often tests the misconception that increasing instance size (Option B) or tuning sink parameters (Option A) will fix latency, when the root cause is a parallelism-to-shard mismatch that only increasing parallelism can resolve.
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 to 8.
The application has 8 shards but only a parallelism of 4, meaning each Flink subtask must process data from 2 shards. This creates a bottleneck because a single subtask cannot process data from multiple shards faster than the slowest shard's throughput. Increasing parallelism to 8 matches the shard count, allowing each subtask to read from exactly one shard, eliminating the contention and reducing latency.
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 batch size in the S3 sink.
Why it's wrong here
Batch size affects file size, not processing latency; Flink processes records continuously.
- ✗
Use a larger Kinesis Data Analytics application instance type.
Why it's wrong here
Larger instance provides more memory/CPU but may not solve parallelism mismatch; also more costly.
- ✓
Increase the parallelism of the Flink application to 8.
Why this is correct
Matching parallelism to shard count ensures each shard is processed concurrently, reducing backpressure.
- ✗
Increase the checkpointing interval to reduce overhead.
Why it's wrong here
Checkpointing interval affects fault tolerance, not throughput; increasing it may reduce latency slightly but not address the core issue.
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
Related to this question
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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 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.)
hard- A.Decrease the number of shards in the source Kinesis stream.
- ✓ B.Use multiple S3 prefixes in the output path to avoid throttling.
- C.Increase the heap memory of the Flink application.
- ✓ D.Increase the checkpoint interval to reduce checkpoint overhead.
- ✓ E.Increase the parallelism of the Flink application.
Why B: 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.
JA
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.