- A
Increase the number of KCL workers to match the number of shards.
Multiple workers can process shards in parallel, reducing per-worker load.
- B
Enable enhanced fan-out for the consumer.
Enhanced fan-out provides dedicated read throughput, eliminating read throttling.
- C
Decrease the checkpoint interval to reduce checkpointing overhead.
Why wrong: More frequent checkpointing increases DynamoDB writes and can cause delays.
- D
Increase the KCL maxRecords parameter to process more records per call.
Processing more records per GetRecords call reduces the number of API calls and improves throughput.
- E
Increase the number of shards in the stream.
Why wrong: More shards increase the write capacity but also increase the number of shards the consumer must process, worsening the issue.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data engineer is troubleshooting a Kinesis Data Streams consumer application that is falling behind. The stream has 10 shards and is receiving 5 MB/s of data. The consumer uses the Kinesis Client Library (KCL) with a single worker. The worker is processing all 10 shards but is experiencing high latency and checkpointing delays. Which THREE actions should the engineer take to improve consumer performance? (Select THREE.)
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 number of KCL workers to match the number of shards.
Option A is correct because the KCL worker is processing all 10 shards sequentially within a single worker, causing a bottleneck. By increasing the number of KCL workers to match the number of shards, each worker can process one shard in parallel, significantly improving throughput and reducing latency. This is a standard scaling pattern for KCL-based consumers.
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 KCL workers to match the number of shards.
Why this is correct
Multiple workers can process shards in parallel, reducing per-worker load.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable enhanced fan-out for the consumer.
Why this is correct
Enhanced fan-out provides dedicated read throughput, eliminating read throttling.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Decrease the checkpoint interval to reduce checkpointing overhead.
Why it's wrong here
More frequent checkpointing increases DynamoDB writes and can cause delays.
- ✓
Increase the KCL maxRecords parameter to process more records per call.
Why this is correct
Processing more records per GetRecords call reduces the number of API calls and improves throughput.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of shards in the stream.
Why it's wrong here
More shards increase the write capacity but also increase the number of shards the consumer must process, worsening the issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think decreasing the checkpoint interval (Option C) reduces overhead, when in fact it increases the frequency of DynamoDB writes and can degrade performance; the correct approach is to increase the checkpoint interval or use asynchronous checkpointing.
Detailed technical explanation
How to think about this question
KCL uses a DynamoDB table for lease coordination and checkpointing; each worker leases one or more shards. With a single worker processing 10 shards, the worker must sequentially poll each shard, process records, and checkpoint, leading to high latency. Increasing workers to match shards allows each worker to independently poll and process its shard, leveraging the KCL's built-in lease balancing. The maxRecords parameter (Option D) controls the number of records fetched per getRecords call; increasing it can reduce the number of API calls and improve throughput, but must be balanced with record processing time to avoid timeouts.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 number of KCL workers to match the number of shards. — Option A is correct because the KCL worker is processing all 10 shards sequentially within a single worker, causing a bottleneck. By increasing the number of KCL workers to match the number of shards, each worker can process one shard in parallel, significantly improving throughput and reducing latency. This is a standard scaling pattern for KCL-based consumers.
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.
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Last reviewed: Jul 4, 2026
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