Question 757 of 724
DVA-C02 Troubleshooting and Optimization Practice Question
An AWS Lambda function processes messages from an Amazon SQS queue and writes results to an Amazon DynamoDB table. The function is configured with a reserved concurrency of 5 and a batch size of 10. CloudWatch metrics show high throttling and a growing queue backlog. The function's execution time averages 1 second per message. What is the MOST effective action to reduce throttling while improving throughput?
⚠ Common exam trap
It's easy for candidates to confuse Lambda throttling with downstream resource throttling (like DynamoDB) and choose to increase write capacity, or they may think increasing batch size alone will solve the problem without considering the concurrency bottleneck.
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 reserved concurrency to 20.
The Lambda function is throttling because its reserved concurrency of 5 limits it to 5 concurrent executions. With a batch size of 10 and 1-second execution time, the function can process at most 5 * 10 = 50 messages per second. Increasing reserved concurrency to 20 allows 20 concurrent executions, raising throughput to 200 messages per second, which directly reduces throttling and clears the backlog.
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 reserved concurrency to 20.
Why this is correct
Increasing reserved concurrency allows Lambda to scale and invoke more function instances concurrently. This directly reduces throttling and allows the function to process more messages from the SQS queue simultaneously, improving throughput and reducing backlog.
- ✗
Increase the batch size to 100.
Why it's wrong here
Increasing batch size may cause each invocation to take longer, potentially leading to timeouts if the function cannot process 100 messages within its timeout. It does not address the underlying concurrency limitation and may even increase throttling because fewer invocations happen but each takes longer.
- ✗
Decrease the reserved concurrency to 2.
Why it's wrong here
Decreasing the reserved concurrency to 2 would be counterproductive as it explicitly caps the number of simultaneous Lambda invocations for this function to just two. If the function is already experiencing throttling due to high message volume from SQS, severely limiting its concurrency would drastically reduce its processing capacity. This action would exacerbate the backlog of messages in the SQS queue, leading to significantly higher message processing latency and potential timeouts.
- ✗
Increase the provisioned write capacity of the DynamoDB table.
Why it's wrong here
The issue is Lambda throttling, not DynamoDB throttling. Increasing DynamoDB capacity does not help Lambda process more messages. It may become relevant later if DynamoDB becomes a bottleneck, but the immediate problem is Lambda throttling.
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 |
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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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