DVA-C02 Troubleshooting and Optimization Practice Question
A developer deployed a new version of an AWS Lambda function that is part of a serverless application. The function uses an Amazon DynamoDB table as a data store. After deployment, the developer notices that the function's latency has increased significantly for some requests. CloudWatch traces show that the increase is due to DynamoDB throttle events. The function is configured with a reserved concurrency of 100 and the DynamoDB table has 5 read capacity units (RCUs) and 5 write capacity units (WCUs). What is the most effective way to reduce the throttling while maintaining application performance?
⚠ Common exam trap
Test-takers frequently choose auto scaling (Option D) thinking it dynamically handles spikes, but they overlook that auto scaling has a significant lag and cannot prevent immediate throttling, whereas increasing the base capacity is the immediate and effective solution.
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 read and write capacity units on the DynamoDB table
The primary cause of the throttling is insufficient DynamoDB capacity to handle the request volume from the Lambda function. Increasing the read and write capacity units (RCUs/WCUs) directly addresses the throttle events by providing more throughput to match the function's concurrency of 100. This is the most effective solution because it resolves the bottleneck at the data store level without reducing the application's ability to process requests concurrently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the reserved concurrency of the Lambda function to 10
Why it's wrong here
Decreasing the Lambda function's reserved concurrency limits the number of concurrent function invocations, thereby reducing the *potential* concurrent requests sent to DynamoDB. However, this action does not increase the DynamoDB table's underlying provisioned capacity (RCU/WCU). If the aggregate request rate from the reduced number of Lambda invocations still exceeds the table's capacity, throttling will persist, merely at a potentially lower frequency, while also slowing down overall event processing.
- ✓
Increase the read and write capacity units on the DynamoDB table
Why this is correct
Increasing the read and write capacity units (RCU/WCU) on the DynamoDB table directly raises its maximum sustained throughput. These units define the number of strongly consistent reads and 1KB writes the table can handle per second. By provisioning more capacity, the table can accommodate a higher volume of operations, directly mitigating throttling errors that occur when request rates exceed the current limits.
- ✗
Enable DynamoDB Accelerator (DAX) for caching reads
Why it's wrong here
DynamoDB Accelerator (DAX) is a fully managed, in-memory cache designed to provide microsecond response times for read-heavy workloads. While DAX can significantly reduce consumed RCUs by serving cached reads, it does not cache write operations. Therefore, enabling DAX would not alleviate any write throttling issues and provides no benefit for write-intensive workloads, adding unnecessary complexity.
- ✗
Enable auto scaling on the DynamoDB table
Why it's wrong here
DynamoDB auto scaling dynamically adjusts the table's provisioned capacity based on target utilization metrics, reacting to sustained changes in workload. However, auto scaling typically takes several minutes to provision additional capacity once a scaling event is triggered. Starting with only 5 units, a sudden spike in traffic would cause immediate and severe throttling *before* auto scaling could react and increase capacity, making it unsuitable for addressing an immediate, active throttling problem.
Visual reference
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.