DEA-C01 Data Ingestion and Transformation Practice Question
A financial services company is building a real-time fraud detection system. Transaction data is ingested via Amazon Kinesis Data Streams and processed by an Amazon Kinesis Data Analytics for Apache Flink application that runs sliding window aggregations. The output is written to an Amazon S3 bucket for downstream analysis. The Flink application is configured with parallelism of 4 and checkpointing every minute. The company has noticed that the application is experiencing high latency and the checkpointing is frequently failing. The CloudWatch metrics show that the Flink application's CPU utilization is near 100% and the checkpoint duration is spiking to over 5 minutes. The data engineer needs to improve performance. 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
✓
Increase the parallelism of the Flink application to distribute the workload across more resources.
Increasing the parallelism of the Flink application allows the workload to be distributed across more resources, which reduces CPU pressure and checkpoint duration. The high CPU utilization and checkpoint spikes indicate that the current parallelism (4) is insufficient for the data volume. Option A is incorrect because increasing shards in the source stream without increasing parallelism may not help if the bottleneck is processing capacity, not ingestion throughput. Option C is incorrect while increasing heap memory might help with state size, the primary issue here is CPU saturation, not memory. Option D is incorrect because decreasing the checkpoint interval would increase checkpoint frequency, potentially worsening failures and 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.
- ✗
Increase the number of shards in the source Kinesis stream to improve throughput.
Why it's wrong here
More shards increase parallelism but CPU may still be bottleneck.
- ✓
Increase the parallelism of the Flink application to distribute the workload across more resources.
Why this is correct
More parallelism can reduce CPU utilization and checkpoint time.
- ✗
Increase the heap memory of the Flink application to handle larger state.
Why it's wrong here
High CPU is the issue, not memory.
- ✗
Decrease the checkpoint interval to 30 seconds to reduce the amount of state being checkpointed.
Why it's wrong here
More frequent checkpoints increase overhead.
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
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