DVA-C02 Development with AWS Services Practice Question
A developer has an AWS Lambda function that processes messages from an Amazon SQS standard queue. The function is idempotent and currently has a batch size of 10. The developer wants to increase throughput and increases the batch size to 100. After the change, CloudWatch metrics show a significant increase in throttles and the queue backlog is growing. The function's reserved concurrency is set to 10. What is the most effective action to resolve the throttling and improve throughput?
⚠ Common exam trap
Candidates often assume throttling is due to function performance (memory or CPU) and choose to increase memory, when in fact the issue is a concurrency limit that prevents the function from scaling to handle the larger batch size.
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 of the Lambda function
Increasing the reserved concurrency from 10 to a higher value directly addresses the root cause of throttling. With a batch size of 100, each invocation processes more messages, but the function's reserved concurrency of 10 limits the maximum number of concurrent executions to 10. This means the Lambda service can only invoke the function 10 times at once, regardless of how many messages are in the queue. By raising reserved concurrency, you allow more concurrent invocations to handle the larger batches, reducing throttling and improving throughput.
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 of the Lambda function
Why this is correct
Increasing reserved concurrency directly allocates a dedicated maximum number of simultaneous executions for this specific Lambda function. This prevents the function from being throttled by the account's unreserved concurrency limit or other functions consuming available capacity. By ensuring more invocations can run in parallel, the function can effectively process larger SQS batch sizes without messages backing up, significantly improving overall message consumption rate and throughput.
- ✗
Increase the memory allocation of the Lambda function
Why it's wrong here
While increasing memory allocation provides more CPU and network bandwidth to each individual Lambda invocation, making it execute faster, it does not address the fundamental issue of concurrency limits. If the function is being throttled because it has reached its maximum allowed concurrent executions, faster individual invocations will not enable more invocations to run simultaneously, thus failing to resolve the backlog of messages in the SQS queue.
- ✗
Switch the SQS queue to a FIFO queue
Why it's wrong here
Switching to an SQS FIFO queue would introduce strict ordering and exactly-once processing guarantees, which inherently come with lower throughput compared to standard queues. FIFO queues are designed for scenarios where message order is paramount, not for maximizing processing volume. This change would likely reduce the rate at which messages can be sent to and processed by Lambda, exacerbating the existing performance bottleneck rather than resolving it.
- ✗
Decrease the batch size back to 10
Why it's wrong here
Decreasing the batch size back to 10 would mean that each Lambda invocation processes fewer messages, requiring a greater number of total invocations to clear the same volume of messages from the SQS queue. While this might reduce the chance of individual invocations timing out or failing due to processing too many messages, it fundamentally lowers the efficiency of the event source mapping. This approach would not improve overall throughput and could even worsen it by increasing the overhead per message processed.
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.