DEA-C01 Data Ingestion and Transformation Practice Question
A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data from a website. The data is consumed by an Amazon Kinesis Data Analytics for Apache Flink application that performs real-time analytics. The Flink application writes its results to an Amazon S3 bucket. The company has noticed that the Flink application is experiencing high checkpoint failure rates, causing delays. The CloudWatch metrics show that the checkpoint size is large and increasing. The data engineer needs to reduce the checkpoint size. Which action should the data engineer take?
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
✓
Enable incremental checkpointing in the Flink application to only write changes since the last checkpoint.
Enabling incremental checkpointing in Flink reduces the amount of data written per checkpoint by only writing changes since the last checkpoint. Option A is wrong because reducing parallelism may increase load per operator. Option B is wrong because decreasing checkpoint interval increases frequency, not size. Option C is wrong because state TTL does not directly reduce checkpoint size.
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 reduce the amount of state accumulated.
Why it's wrong here
More frequent checkpoints may increase overhead.
- ✗
Reduce the parallelism of the Flink application.
Why it's wrong here
Reducing parallelism may increase state per operator, worsening the issue.
- ✗
Increase the state time-to-live (TTL) configuration to retain state longer.
Why it's wrong here
Longer TTL increases state size.
- ✓
Enable incremental checkpointing in the Flink application to only write changes since the last checkpoint.
Why this is correct
Incremental checkpoints reduce size and improve performance.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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