Question 1,174 of 724
DVA-C02 DynamoDB Auto Scaling Practice Question
A developer is building a serverless application using AWS Lambda and Amazon API Gateway. The API has a REST endpoint that triggers a Lambda function to write data to an Amazon DynamoDB table. Under high traffic, some requests are failing with 5xx errors. The developer notices that the Lambda function's duration is spiking. Which combination of actions should the developer take to improve performance and reduce errors?
⚠ Common exam trap
The trap is that DAX is often mistakenly applied to improve write performance, but it only caches reads. Candidates may also overlook the effectiveness of increasing Lambda memory to reduce duration, and they might not consider DynamoDB auto-scaling as a direct solution for write throttling.
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's memory and enable DynamoDB auto-scaling.
Increasing the Lambda function's memory allocation also increases CPU and network throughput, which can reduce execution duration and prevent timeouts. Enabling DynamoDB auto-scaling allows the table to handle write capacity bursts, reducing throttling and subsequent 5xx errors. Option A is incorrect because DynamoDB Accelerator (DAX) is a read cache and does not improve write performance. Option B introduces unnecessary latency and does not directly address write capacity. Option D is focused on read performance and does not help with write-intensive workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable DynamoDB Accelerator (DAX) for the table and set a Lambda reserved concurrency.
Why it's wrong here
Incorrect. DynamoDB Accelerator (DAX) is a read cache and does not improve write performance. While Lambda reserved concurrency helps, DAX is not appropriate for write operations.
- ✗
Use an Amazon SQS queue as a buffer between API Gateway and Lambda.
Why it's wrong here
Incorrect. Using an SQS queue decouples the request but adds latency and does not directly address write capacity or duration spikes. It is not the best combination for this scenario.
- ✓
Increase the Lambda function's memory and enable DynamoDB auto-scaling.
Why this is correct
Correct. Increasing Lambda memory reduces execution duration, and DynamoDB auto-scaling prevents write throttling, together reducing 5xx errors and improving performance.
- ✗
Switch the API endpoint to HTTP API and enable API Gateway caching.
Why it's wrong here
Incorrect. Switching to HTTP API reduces latency but does not affect write capacity. API Gateway caching is for read responses, not writes, so it does not help.
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: Jul 4, 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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