- A
Increase the number of shards in the Kinesis stream
Why wrong: More shards increase input rate; if the application can't keep up, it worsens latency.
- B
Increase the Parallelism of the Flink application
Higher parallelism allows more concurrent processing.
- C
Enable exactly-once delivery to S3
Why wrong: Exactly-once adds overhead and may increase latency.
- D
Use a larger Kinesis Data Analytics application (increase KPU)
Why wrong: While increasing KPU can help, parallelism is more direct; KPU may be limited by default.
Question 1,560 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis stream with 10 shards and writes to an S3 bucket. The application is experiencing high latency. Analysis shows that the application is not keeping up with the incoming data rate. Which action would MOST effectively reduce latency?
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
The high latency is caused by the Flink application not keeping up with the incoming data rate, which indicates a processing bottleneck within the application itself. Increasing the Parallelism of the Flink application (Option B) directly increases the number of parallel subtasks that can process data concurrently, improving throughput and reducing latency. This is the most effective action because it addresses the root cause—insufficient compute resources for stream processing—without changing the source or sink configuration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 Kinesis stream
Why it's wrong here
More shards increase input rate; if the application can't keep up, it worsens latency.
- ✓
Increase the Parallelism of the Flink application
Why this is correct
Higher parallelism allows more concurrent processing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable exactly-once delivery to S3
Why it's wrong here
Exactly-once adds overhead and may increase latency.
- ✗
Use a larger Kinesis Data Analytics application (increase KPU)
Why it's wrong here
While increasing KPU can help, parallelism is more direct; KPU may be limited by default.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling the infrastructure (KPU or shards) with scaling the application logic (parallelism), assuming that more shards or larger instances automatically resolve processing bottlenecks without explicitly tuning the Flink application's parallelism.
Detailed technical explanation
How to think about this question
Flink's parallelism determines the number of parallel task slots that process data streams; each shard in Kinesis can be consumed by at most one Flink subtask, so the parallelism should be at least equal to the number of shards to avoid backpressure. Under the hood, Flink uses a checkpointing mechanism with barriers to ensure consistency, and increasing parallelism distributes the operator state across more subtasks, reducing per-task load. In real-world scenarios, simply increasing KPU without adjusting parallelism often leads to underutilized resources and persistent latency issues.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the Parallelism of the Flink application — The high latency is caused by the Flink application not keeping up with the incoming data rate, which indicates a processing bottleneck within the application itself. Increasing the Parallelism of the Flink application (Option B) directly increases the number of parallel subtasks that can process data concurrently, improving throughput and reducing latency. This is the most effective action because it addresses the root cause—insufficient compute resources for stream processing—without changing the source or sink configuration.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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