- A
Increase the batch size to 1000
Why wrong: A larger batch size may increase execution time further, potentially causing the function to exceed the timeout. It does not address the per-record processing bottleneck.
- B
Increase the parallelization factor to 10
The parallelization factor determines the number of concurrent Lambda invocations per shard. Increasing it allows multiple invocations to process records from the same shard simultaneously, dramatically increasing throughput without additional shard costs.
- C
Increase the memory of the Lambda function
Why wrong: Increasing memory may improve performance if the function is CPU-bound, but it is not guaranteed and increases cost. The function is already near its timeout, indicating a potential bottleneck in record processing logic.
- D
Split the stream into more shards
Why wrong: Splitting the stream increases the number of shards, leading to more Lambda invocations but also higher Kinesis costs. This is less cost-effective than increasing the parallelization factor.
Quick Answer
The answer is to increase the parallelization factor to 10. This is the most cost-effective solution because it allows each of the 10 shards to be processed by up to 10 concurrent Lambda invocations, directly addressing the slow processing and rising iterator age without adding more shards or incurring additional stream costs. Since the function is not throttled or erroring, the bottleneck is the per-invocation execution time near the 5-minute timeout; parallelization lets multiple batches from the same shard run simultaneously, boosting throughput. On the AWS Certified Developer Associate DVA-C02 exam, this scenario tests your understanding of how Lambda’s parallelization factor decouples concurrency from shard count—a common trap is thinking you must increase shards or batch size, but the factor is a free lever. Remember: for a slow, non-throttled function, “parallelize the factor, not the 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. A key principle to apply: the parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard.. 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 function is processing records slowly, and the iterator age is increasing. 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. 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 to 10
Increasing the parallelization factor to 10 allows each shard to be processed by up to 10 concurrent Lambda invocations, which directly increases throughput without additional shard costs. Since the function is not throttled or erroring, the bottleneck is the per-invocation processing time; parallelization reduces the iterator age by processing multiple batches per shard simultaneously.
Key principle: The parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard.
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 1000
Why it's wrong here
A larger batch size may increase execution time further, potentially causing the function to exceed the timeout. It does not address the per-record processing bottleneck.
- ✓
Increase the parallelization factor to 10
Why this is correct
The parallelization factor determines the number of concurrent Lambda invocations per shard. Increasing it allows multiple invocations to process records from the same shard simultaneously, dramatically increasing throughput without additional shard costs.
Related concept
The parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard.
- ✗
Increase the memory of the Lambda function
Why it's wrong here
Increasing memory may improve performance if the function is CPU-bound, but it is not guaranteed and increases cost. The function is already near its timeout, indicating a potential bottleneck in record processing logic.
- ✗
Split the stream into more shards
Why it's wrong here
Splitting the stream increases the number of shards, leading to more Lambda invocations but also higher Kinesis costs. This is less cost-effective than increasing the parallelization factor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing shards is the only way to scale Kinesis processing, but the parallelization factor is a cost-effective Lambda-specific tuning knob that increases concurrency without additional shard costs.
Detailed technical explanation
How to think about this question
The parallelization factor controls the maximum number of concurrent batches per shard; with a factor of 10, each shard can have up to 10 invocations processing different batches simultaneously, effectively multiplying throughput by up to 10x. This is distinct from increasing shard count, which would require re-sharding and increase per-shard costs. In practice, if the function is I/O-bound (e.g., writing to a downstream service), parallelization can dramatically reduce iterator age without code changes.
KKey Concepts to Remember
- The parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard.
- It can be configured from 1 to 10 for Kinesis and DynamoDB streams.
- Each parallel invocation processes a distinct range of sequence numbers within the shard.
- Increasing the parallelization factor improves throughput without increasing Kinesis shard costs.
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
The parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard.
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
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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 — The parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard..
What is the correct answer to this question?
The correct answer is: Increase the parallelization factor to 10 — Increasing the parallelization factor to 10 allows each shard to be processed by up to 10 concurrent Lambda invocations, which directly increases throughput without additional shard costs. Since the function is not throttled or erroring, the bottleneck is the per-invocation processing time; parallelization reduces the iterator age by processing multiple batches per shard simultaneously.
What should I do if I get this DVA-C02 question wrong?
Review the parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard., then practise related DVA-C02 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
The parallelization factor allows multiple concurrent Lambda invocations per Kinesis shard.
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Same concept, more angles
1 more ways this is tested on DVA-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Increase the batch size to 500
- B.Increase the number of shards
- C.Increase the timeout to 10 minutes
- ✓ D.Increase the parallelization factor per shard
Why D: 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.
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Last reviewed: Jun 11, 2026
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