The answer is to increase the number of KCL workers per shard, such as running two workers per shard. This is correct because the Kinesis Client Library (KCL) with its default configuration assigns only a single worker per shard, and that worker processes records sequentially, creating a bottleneck when records are small (1 KB) and the shard count is fixed at 10. By adding more workers per shard, you enable parallel processing of the same shard’s records, directly reducing consumer lag when the constraint is CPU or processing time rather than the shard’s throughput limit. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of KCL’s default behavior and the distinction between scaling shards and scaling consumers—a common trap is to increase shard count unnecessarily when the real fix is worker parallelism. Remember the memory tip: “One worker per shard is the default; for lag, add workers, not shards.”
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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.
An Amazon Kinesis Data Streams application is lagging behind. The data records are small (1 KB) and the shard count is 10. The consumer uses the KCL with default configuration. Which action will MOST effectively reduce the consumer lag?
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 per shard (e.g., 2 workers per shard).
Option A is correct because the KCL (Kinesis Client Library) uses a single worker per shard by default, and each worker processes records sequentially within that shard. Increasing the number of workers per shard (e.g., 2 workers) allows parallel processing of the same shard’s records, directly reducing consumer lag when records are small (1 KB) and the bottleneck is CPU or processing time per record, not throughput limits.
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 per shard (e.g., 2 workers per shard).
Why this is correct
More workers can process records concurrently, reducing lag.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Enhanced Fan-Out to provide dedicated throughput.
Why it's wrong here
Enhanced Fan-Out is for multiple consumers, not for reducing lag of one consumer.
✗
Increase the number of shards to 20.
Why it's wrong here
More shards require more consumers; otherwise, lag may increase.
✗
Reduce the record size by compressing the data.
Why it's wrong here
Record size is already small; compression adds overhead.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing shards (Option C) always reduces lag, but they miss that KCL workers are per-shard by default, so more shards only help if the shard is saturated with data, not when the consumer is slow at processing each record.
Detailed technical explanation
How to think about this question
Under the hood, the KCL uses a single-threaded record processor per shard by default, and each record is processed sequentially via the `processRecords` method. Increasing workers per shard (using `maxRecords` or multiple `RecordProcessor` instances) allows concurrent processing of records from the same shard, which is effective when records are small and the consumer is CPU-bound. In real-world scenarios, this is common when the consumer performs heavy transformations or external API calls per record, making parallelization within a shard the most direct way to reduce lag without changing the shard topology.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Operations and Support — This question tests Data Operations and Support — 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 per shard (e.g., 2 workers per shard). — Option A is correct because the KCL (Kinesis Client Library) uses a single worker per shard by default, and each worker processes records sequentially within that shard. Increasing the number of workers per shard (e.g., 2 workers) allows parallel processing of the same shard’s records, directly reducing consumer lag when records are small (1 KB) and the bottleneck is CPU or processing time per record, not throughput limits.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.