- A
Increase the batch size to 500
Why wrong: Increasing batch size may cause the function to exceed the 5-minute timeout if processing per record is high, and may not reduce the iterator age if the function is already near timeout.
- B
Increase the number of shards
Why wrong: Adding shards increases processing capacity but also increases Kinesis stream cost. It is not the most cost-effective solution.
- C
Increase the timeout to 10 minutes
Why wrong: This only moves the timeout boundary; it does not increase throughput. The function may still be slow, and the iterator age may continue to rise.
- D
Increase the parallelization factor per shard
Increasing the parallelization factor allows the function to process multiple batches from the same shard concurrently, improving throughput without additional cost from more shards.
DVA-C02 Troubleshooting and Optimization Practice Question
This DVA-C02 practice question tests your understanding of troubleshooting and optimization. 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 developer is troubleshooting an AWS Lambda function that processes records from an Amazon Kinesis Data Stream. The function is configured with a batch size of 100 and a parallelization factor of 1. The iterator age metric is increasing, and CloudWatch Logs show the function execution time is around 4 minutes (timeout is 5 minutes). The stream has 10 shards. What is the most cost-effective way to increase processing throughput?
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 parallelization factor per shard
Increasing the parallelization factor per shard (option D) allows each shard to be processed by multiple Lambda instances concurrently, which directly increases throughput without requiring additional shards or changes to the stream. Since the function is not hitting the 5-minute timeout but is taking ~4 minutes per batch, the bottleneck is processing concurrency per shard, not batch size or execution duration. This is the most cost-effective solution because it uses existing shards and avoids the cost of additional shards or unnecessary timeout increases.
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 batch size to 500
Why it's wrong here
Increasing batch size may cause the function to exceed the 5-minute timeout if processing per record is high, and may not reduce the iterator age if the function is already near timeout.
- ✗
Increase the number of shards
Why it's wrong here
Adding shards increases processing capacity but also increases Kinesis stream cost. It is not the most cost-effective solution.
- ✗
Increase the timeout to 10 minutes
Why it's wrong here
This only moves the timeout boundary; it does not increase throughput. The function may still be slow, and the iterator age may continue to rise.
- ✓
Increase the parallelization factor per shard
Why this is correct
Increasing the parallelization factor allows the function to process multiple batches from the same shard concurrently, improving throughput without additional cost from more shards.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing batch size (option A) is the natural fix for slow processing, but they overlook that the function is already near its timeout limit, making a larger batch size impractical without also increasing the timeout.
Detailed technical explanation
How to think about this question
The parallelization factor (default 1) controls how many Lambda instances can process a single shard concurrently. With a factor of 1, each shard is processed sequentially, so with 10 shards, at most 10 Lambda instances run at a time. Increasing the parallelization factor to, say, 2 allows up to 20 concurrent instances (2 per shard), effectively doubling throughput without adding shards. However, note that Kinesis guarantees record ordering within a shard only when the parallelization factor is 1; increasing it may cause out-of-order processing, which is acceptable for many use cases but must be considered.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DVA-C02 question test?
Troubleshooting and Optimization — This question tests Troubleshooting and Optimization — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the parallelization factor per shard — Increasing the parallelization factor per shard (option D) allows each shard to be processed by multiple Lambda instances concurrently, which directly increases throughput without requiring additional shards or changes to the stream. Since the function is not hitting the 5-minute timeout but is taking ~4 minutes per batch, the bottleneck is processing concurrency per shard, not batch size or execution duration. This is the most cost-effective solution because it uses existing shards and avoids the cost of additional shards or unnecessary timeout increases.
What should I do if I get this DVA-C02 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: Jun 11, 2026
This DVA-C02 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 DVA-C02 exam.
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