Question 1,297 of 724
DVA-C02 Lambda batch size Practice Question
A developer has an AWS Lambda function that processes messages from an Amazon SQS queue. The function is configured with a batch size of 10, reserved concurrency of 5, and a timeout of 5 minutes. The SQS queue has a large backlog, and CloudWatch metrics show high throttling (Throttles) for the Lambda function. The function is idempotent and can process up to 100 messages in a single invocation. What is the MOST effective way to increase throughput without increasing the reserved concurrency?
⚠ Common exam trap
Many candidates think increasing reserved concurrency is the only way to improve throughput, but the question explicitly forbids that, and they overlook that increasing the batch size can achieve the same goal by processing more messages per invocation without adding more concurrent executions.
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 batch size to 100.
Increasing the batch size to 100 allows each Lambda invocation to process up to 100 messages from the SQS queue instead of the current 10. Since the function is idempotent and can handle 100 messages per invocation, this change maximizes the number of messages processed per invocation without altering the reserved concurrency of 5. With a batch size of 100, each of the 5 concurrent invocations can process up to 100 messages, yielding a potential throughput of 500 messages per invocation cycle, which directly reduces the backlog and throttling by consuming messages faster.
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 100.
Why this is correct
Increasing the batch size for an SQS event source mapping allows each AWS Lambda invocation to process a larger number of messages simultaneously. This significantly reduces the total number of Lambda invocations required to process a given volume of messages, thereby lowering the demand for concurrent executions. By processing more work per invocation, the function is less likely to hit its concurrency limit and experience throttling, effectively optimizing resource utilization without increasing reserved concurrency.
- ✗
Increase the reserved concurrency to 10.
Why it's wrong here
While increasing the reserved concurrency to 10 would indeed allow more concurrent Lambda invocations to process messages, potentially alleviating throttling issues, the problem statement explicitly prohibits increasing reserved concurrency. Therefore, despite its technical effectiveness in isolation for handling higher message volumes, this option violates a critical constraint of the problem, rendering it an invalid solution within the given parameters.
- ✗
Reduce the batch size to 1.
Why it's wrong here
Reducing the batch size to 1 would force the AWS Lambda function to invoke for almost every single message received from the SQS queue. This drastic increase in the total number of Lambda invocations required to process the same message volume would quickly consume the available concurrency. Consequently, this action would exacerbate the existing throttling issues and lead to significantly lower overall throughput, making it counterproductive to the goal of improving processing efficiency.
- ✗
Enable the SQS queue to use long polling.
Why it's wrong here
Enabling long polling on an Amazon SQS queue primarily helps reduce the number of empty receive message responses and associated costs by allowing the API call to wait for messages to arrive. However, long polling does not directly influence the rate at which a Lambda function processes messages once invoked, nor does it increase the function's available concurrency or reduce throttling experienced by the Lambda service itself. The core issue of Lambda throttling due to concurrent execution limits remains unaddressed by this SQS queue configuration.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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 →
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.
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.