DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The Flink application reads from a Kinesis Data Streams source, performs aggregations, and writes results to Amazon S3. The application is experiencing high checkpoint failures, and the processing lag is increasing. The data volume is 50 MB/s with an average record size of 1 KB. Which TWO actions would improve checkpoint reliability and reduce lag? (Choose TWO.)
⚠ Common exam trap
It's easy for candidates to think decreasing the checkpoint interval will speed up checkpoints, but in reality, it increases overhead and failure rates, while increasing parallelism (Option C) seems intuitive but actually reduces per-task resources and can worsen backpressure.
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 checkpoint interval in the Flink configuration.
Increasing the checkpoint interval (Option D) reduces the frequency of checkpoint operations, which decreases the overhead on the Flink application and allows it to dedicate more resources to processing data, thereby reducing lag. This is especially effective when checkpoint failures are caused by the system being unable to complete checkpoints within the current interval due to high throughput (50 MB/s).
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 checkpoint interval to complete checkpoints faster.
Why it's wrong here
More frequent checkpoints increase failures.
- ✗
Replace the S3 sink with Kinesis Data Firehose.
Why it's wrong here
Does not address checkpoint issues.
- ✗
Decrease the parallelism of the Flink application.
Why it's wrong here
Would increase lag.
- ✓
Increase the checkpoint interval in the Flink configuration.
Why this is correct
Less frequent checkpoints reduce overhead.
- ✓
Increase the number of Kinesis Processing Units (KPUs) for the application.
Why this is correct
More resources reduce lag.
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.