DVA-C02 Troubleshooting and Optimization Practice Question
A developer is troubleshooting an AWS Lambda function that processes records from an Amazon Kinesis Data Stream. The function is configured with a batch size of 100 and a parallelization factor of 1. The iterator age metric is increasing, and CloudWatch Logs show the function execution time is around 4 minutes (timeout is 5 minutes). The stream has 10 shards. What is the most cost-effective way to increase processing throughput?
⚠ Common exam trap
It's easy for candidates to assume increasing batch size (option A) is the natural fix for slow processing, but they overlook that the function is already near its timeout limit, making a larger batch size impractical without also increasing the timeout.
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 parallelization factor per shard
Increasing the parallelization factor per shard (option D) allows each shard to be processed by multiple Lambda instances concurrently, which directly increases throughput without requiring additional shards or changes to the stream. Since the function is not hitting the 5-minute timeout but is taking ~4 minutes per batch, the bottleneck is processing concurrency per shard, not batch size or execution duration. This is the most cost-effective solution because it uses existing shards and avoids the cost of additional shards or unnecessary timeout increases.
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 500
Why it's wrong here
While increasing the batch size can reduce the overhead of Lambda invocations, it also means each invocation must process more records. If the Lambda function is already struggling with the processing time per record, a larger batch will exacerbate the issue, potentially causing invocations to time out and increasing the iterator age as the function falls further behind. This does not improve overall throughput if the bottleneck is the processing speed of individual records.
- ✗
Increase the number of shards
Why it's wrong here
Increasing the number of Kinesis shards directly increases the stream's total throughput capacity and allows for more concurrent Lambda invocations, as each shard can be processed independently. However, Kinesis Data Streams are billed per shard-hour, meaning adding shards significantly increases the operational cost of the stream. While effective for scaling, it is not the most cost-effective solution compared to optimizing the Lambda function's processing efficiency within existing stream resources.
- ✗
Increase the timeout to 10 minutes
Why it's wrong here
Extending the Lambda function's timeout merely allows a single invocation to run for a longer duration before being terminated by the service. It does not inherently increase the rate at which records are processed or the number of concurrent invocations. If the function is slow due to inefficient code or resource constraints, increasing the timeout will only delay the inevitable timeout or allow it to fall further behind, leading to a continuously rising iterator age without improving overall throughput.
- ✓
Increase the parallelization factor per shard
Why this is correct
Increasing the parallelization factor per shard for a Kinesis stream event source mapping allows a single Lambda function to process multiple concurrent batches from the *same* shard. By default, Lambda processes one batch per shard concurrently. Raising this factor (up to 10) directly boosts the effective processing throughput from each shard without incurring additional Kinesis shard costs, making it a highly efficient way to reduce iterator age and catch up on backlog.
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.