- A
Enable DynamoDB Accelerator (DAX) for the table and set a Lambda reserved concurrency.
Why wrong: 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.
- B
Use an Amazon SQS queue as a buffer between API Gateway and Lambda.
Why wrong: 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.
- C
Increase the Lambda function's memory and enable DynamoDB auto-scaling.
Correct. Increasing Lambda memory reduces execution duration, and DynamoDB auto-scaling prevents write throttling, together reducing 5xx errors and improving performance.
- D
Switch the API endpoint to HTTP API and enable API Gateway caching.
Why wrong: 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.
Improving Lambda and DynamoDB Performance Under High Traffic | AWS Developer Associate Explained
This DVA-C02 practice question tests your understanding of development with aws services. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: dynamoDB Auto Scaling. 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 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?
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.
Option C is correct because 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.
Key principle: DynamoDB Auto Scaling
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.
Related concept
DynamoDB Auto Scaling
- ✗
Switch the API endpoint to HTTP API and enable API Gateway caching.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Scenario analysis trap
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.
Detailed technical explanation
How to think about this question
DAX is an in-memory cache for DynamoDB that can reduce read response times from milliseconds to microseconds, but it does not cache write operations; however, by offloading read traffic, it frees up read capacity units (RCUs) for other operations, indirectly reducing throttling. Lambda reserved concurrency sets a hard limit on the number of concurrent executions for a function, preventing it from overwhelming downstream resources like DynamoDB and avoiding the 'ConcurrentInvocationLimitExceeded' error. In practice, a common scenario is a flash crowd causing DynamoDB to throttle writes, leading to retries that increase Lambda duration and cause timeouts, which DAX and reserved concurrency mitigate.
KKey Concepts to Remember
- DynamoDB Auto Scaling
- Lambda Memory and CPU
- Lambda Reserved Concurrency
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
DynamoDB Auto Scaling
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.
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 |
What to study next
Got this wrong? Here's your next step.
Review dynamoDB Auto Scaling, 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?
Development with AWS Services — This question tests Development with AWS Services — DynamoDB Auto Scaling.
What is the correct answer to this question?
The correct answer is: Increase the Lambda function's memory and enable DynamoDB auto-scaling. — Option C is correct because 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.
What should I do if I get this DVA-C02 question wrong?
Review dynamoDB Auto Scaling, then practise related DVA-C02 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
DynamoDB Auto Scaling
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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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