Jeff’s Note #
Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Lead Developer.
For DVA-C02 candidates, the confusion often lies in how Lambda CPU allocation is tied to memory settings rather than separate CPU quotas. In production, this is about knowing exactly how to optimize Lambda performance by tuning memory size, which indirectly scales CPU power. Let’s drill down.
The Certification Drill (Simulated Question) #
Scenario #
DevTech Solutions is building a serverless backend using Amazon API Gateway integrated with AWS Lambda functions. The team notices that under increasing load, the Lambda function invoked by the API Gateway exhibits high latency and is unable to process requests promptly. They have identified that the Lambda execution environment’s CPU capacity is insufficient to sustain the workload.
The Requirement: #
Before launching this API to production, the lead developer must configure the Lambda function to have more CPU power to reduce execution latency.
The Options #
- A) Increase the virtual CPU (vCPU) cores quota of the Lambda function.
- B) Increase the amount of memory that is allocated to the Lambda function.
- C) Increase the ephemeral storage size of the Lambda function.
- D) Increase the timeout value of the Lambda function.
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Correct Answer #
B) Increase the amount of memory that is allocated to the Lambda function.
Quick Insight: The Developer Imperative #
- AWS Lambda CPU resources are allocated in proportion to the memory size set for the function. Increasing memory automatically provisions more CPU power.
- Lambda does not have an explicit vCPU quota setting — memory allocation drives CPU performance scaling.
- Ephemeral storage affects temporary disk space, not CPU.
- Timeout controls max runtime, not CPU capacity.
Content Locked: The Expert Analysis #
You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?
The Expert’s Analysis #
Correct Answer #
Option B
The Winning Logic #
AWS Lambda allocates CPU power proportional to the memory configured for the function. When you increase the memory setting (from 128MB up to 10GB), the Lambda runtime automatically allocates a proportional amount of CPU power, network bandwidth, and other resources. This implicit coupling means boosting memory is the documented and recommended way to scale CPU performance inside Lambda.
- This change affects the entire Lambda environment scaling.
- It directly reduces execution time and latency for CPU-bound functions.
- No separate API or quota exists to increase vCPUs independently.
The Trap (Distractor Analysis) #
- Why not A? Lambda does not expose a virtual CPU core quota to configure or increase. CPU allocation is implicit and tied to memory size, so attempting this option shows a misunderstanding of Lambda’s resource model.
- Why not C? Increasing ephemeral storage only enlarges local disk space available during invocation (up to 10GB), which has no impact on CPU or memory performance.
- Why not D? Increasing the timeout extends the max allowed runtime but does nothing to speed up CPU processing or reduce latency.
The Technical Blueprint #
For Developer / SysOps (Code/CLI Snippet) #
You can update Lambda memory size via the AWS CLI as follows:
aws lambda update-function-configuration --function-name MyFunction \
--memory-size 1024
This command sets the Lambda memory to 1024 MB, thereby increasing CPU power proportionally.
The Comparative Analysis #
| Option | API Complexity | Performance Impact | Use Case |
|---|---|---|---|
| A | N/A | None (Invalid) | Misconception, no such setting |
| B | Simple CLI/API | High | Correct way to boost Lambda CPU |
| C | Simple CLI/API | None | Increases disk space only |
| D | Simple CLI/API | None | Extends function runtime limit |
Real-World Application (Practitioner Insight) #
Exam Rule #
For the exam, always pick Increase Memory when the question implies boosting Lambda CPU or reducing latency due to CPU limitations.
Real World #
In production, tuning memory often balances performance and cost. Remember, increasing memory increases billed duration cost, but can significantly reduce runtime by adding CPU power.
(CTA) Stop Guessing, Start Mastering #
Disclaimer
This is a study note based on simulated scenarios for the AWS DVA-C02 exam.