- A
Increase the batch size to 500.
Why wrong: If the function is already close to timeout with a batch size of 100, increasing the batch size will likely cause it to timeout more often, worsening throughput.
- B
Increase the parallelization factor to 10.
Why wrong: While this allows up to 100 concurrent invocations, each invocation still takes near 5 minutes. The total records processed per second may not increase significantly if each batch takes the same time.
- C
Increase the Lambda function memory and CPU allocation.
Increasing memory increases CPU allocation proportionally, which can make each invocation faster. This reduces the per-batch processing time, allowing the function to keep up with the stream and decrease the iterator age.
- D
Split the stream into more shards.
Why wrong: Adding more shards increases the number of concurrent Lambda invocations, but each invocation still processes slowly. The throughput per shard remains low, so the overall throughput may not improve enough.
Quick Answer
The answer is to increase the Lambda function memory and CPU allocation. This is correct because Lambda’s architecture ties CPU power directly to memory: doubling the memory also doubles the available vCPU, which directly reduces execution time for CPU-bound operations. Since the iterator age is increasing and execution time is already near the 5-minute timeout with no errors or throttling, the bottleneck is clearly computational, not I/O or concurrency. On the AWS Certified Developer Associate DVA-C02 exam, this scenario tests your understanding that Lambda throughput optimization often requires adjusting memory rather than batch size or shard count—a common trap is to assume adding shards or increasing batch size will help, but those only matter if the function has spare capacity. Remember the memory-CPU link: for CPU-bound Kinesis processing, more memory means more CPU, which means faster batches and lower iterator age.
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. A key principle to apply: aWS Lambda CPU and network bandwidth scale proportionally with allocated memory.. 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 developer notices that the iterator age is increasing, indicating that the function is not keeping up with the stream. CloudWatch Logs show that the function is not experiencing errors or throttling, but the execution time per invocation is close to the 5-minute timeout. The stream has 10 shards. Which action will most likely increase processing throughput?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 Lambda function memory and CPU allocation.
Option C is correct because the function's execution time is already near the 5-minute timeout, indicating a CPU-bound or memory-bound operation. Increasing memory proportionally increases CPU allocation in Lambda, which directly reduces execution time per invocation, allowing each batch to be processed faster and thus increasing overall throughput without changing the batch size or shard count.
Key principle: AWS Lambda CPU and network bandwidth scale proportionally with allocated memory.
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
If the function is already close to timeout with a batch size of 100, increasing the batch size will likely cause it to timeout more often, worsening throughput.
- ✗
Increase the parallelization factor to 10.
Why it's wrong here
While this allows up to 100 concurrent invocations, each invocation still takes near 5 minutes. The total records processed per second may not increase significantly if each batch takes the same time.
- ✓
Increase the Lambda function memory and CPU allocation.
Why this is correct
Increasing memory increases CPU allocation proportionally, which can make each invocation faster. This reduces the per-batch processing time, allowing the function to keep up with the stream and decrease the iterator age.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
AWS Lambda CPU and network bandwidth scale proportionally with allocated memory.
- ✗
Split the stream into more shards.
Why it's wrong here
Adding more shards increases the number of concurrent Lambda invocations, but each invocation still processes slowly. The throughput per shard remains low, so the overall throughput may not improve enough.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume increasing parallelism (via shards or parallelization factor) always improves throughput, but when the bottleneck is per-invocation execution time (not concurrency), only reducing that time—by increasing memory/CPU—will help.
Detailed technical explanation
How to think about this question
AWS Lambda allocates CPU proportionally to the configured memory, up to 10 GB, with a linear relationship (e.g., 1,769 MB gives one full vCPU). For CPU-bound workloads, increasing memory reduces execution time directly, often more than linearly due to improved parallelism within the function. In Kinesis processing, the iterator age increases when the consumer's processing rate (records/second) is less than the stream's write rate; reducing per-record processing time by boosting CPU is the most direct fix when errors and throttling are absent.
KKey Concepts to Remember
- AWS Lambda CPU and network bandwidth scale proportionally with allocated memory.
- Increasing Lambda memory can significantly reduce execution time for CPU-bound or memory-bound workloads.
- Faster Lambda execution time directly improves processing throughput for event sources like Kinesis.
- Reducing execution time helps decrease Kinesis iterator age, indicating the function is keeping up with the stream.
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
AWS Lambda CPU and network bandwidth scale proportionally with allocated memory.
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. AWS Lambda CPU and network bandwidth scale proportionally with allocated memory. 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.
Review aWS Lambda CPU and network bandwidth scale proportionally with allocated memory., then practise related DVA-C02 questions on the same topic to reinforce the concept.
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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 — AWS Lambda CPU and network bandwidth scale proportionally with allocated memory..
What is the correct answer to this question?
The correct answer is: Increase the Lambda function memory and CPU allocation. — Option C is correct because the function's execution time is already near the 5-minute timeout, indicating a CPU-bound or memory-bound operation. Increasing memory proportionally increases CPU allocation in Lambda, which directly reduces execution time per invocation, allowing each batch to be processed faster and thus increasing overall throughput without changing the batch size or shard count.
What should I do if I get this DVA-C02 question wrong?
Review aWS Lambda CPU and network bandwidth scale proportionally with allocated memory., then practise related DVA-C02 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
AWS Lambda CPU and network bandwidth scale proportionally with allocated memory.
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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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