- A
Low number of shards in the Kinesis stream.
Fewer shards mean less parallelism, leading to throttling and retries.
- B
High batch size in the event source mapping.
Large batch sizes increase processing time per invocation.
- C
High concurrency limit for the Lambda function.
Why wrong: High concurrency allows more parallel processing, reducing latency.
- D
Using Lambda@Edge to process the records.
Why wrong: Lambda@Edge is not used for Kinesis processing; it's for CloudFront.
- E
Insufficient memory allocated to the Lambda function.
Less memory can lead to slower execution, especially for CPU-bound tasks.
Quick Answer
The answer is three factors: insufficient memory, high batch size, and low shard count. Insufficient memory allocated to the Lambda function directly slows execution, as CPU and network throughput scale with memory, making under-provisioning a common cause of high latency in Kinesis Lambda processing. A high batch size forces the function to process more records per invocation, increasing per-call duration, while a low Kinesis shard count limits throughput and triggers throttling, leading to retries and compounding delays. On the AWS Certified Developer Associate DVA-C02 exam, this question tests your understanding of Lambda’s integration with Kinesis streams, often trapping candidates who confuse concurrency with shard count—higher concurrency reduces latency, not increases it. Remember the mnemonic: “Shards, Size, Memory—three levers for latency.”
DVA-C02 Troubleshooting and Optimization Practice Question
This DVA-C02 practice question tests your understanding of troubleshooting and optimization. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 investigating why an AWS Lambda function that processes Kinesis records is experiencing high latency. Which THREE factors could contribute to this? (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
Low number of shards in the Kinesis stream.
High batch size can cause longer processing time per invocation. Insufficient memory slows execution. A low Kinesis shard count can cause throttling and retries, increasing latency. Option B is wrong because higher concurrency (within limits) reduces latency, not increases. Option E is wrong because Lambda@Edge is for CloudFront, not Kinesis.
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.
- ✓
Low number of shards in the Kinesis stream.
Why this is correct
Fewer shards mean less parallelism, leading to throttling and retries.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
High batch size in the event source mapping.
Why this is correct
Large batch sizes increase processing time per invocation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
High concurrency limit for the Lambda function.
Why it's wrong here
High concurrency allows more parallel processing, reducing latency.
- ✗
Using Lambda@Edge to process the records.
Why it's wrong here
Lambda@Edge is not used for Kinesis processing; it's for CloudFront.
- ✓
Insufficient memory allocated to the Lambda function.
Why this is correct
Less memory can lead to slower execution, especially for CPU-bound tasks.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 DVA-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Troubleshooting and Optimization — study guide chapter
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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: Low number of shards in the Kinesis stream. — High batch size can cause longer processing time per invocation. Insufficient memory slows execution. A low Kinesis shard count can cause throttling and retries, increasing latency. Option B is wrong because higher concurrency (within limits) reduces latency, not increases. Option E is wrong because Lambda@Edge is for CloudFront, not Kinesis.
What should I do if I get this DVA-C02 question wrong?
Identify which DVA-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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