SAA-C03 Design Secure Architectures Practice Question
A Lambda function processes CPU-heavy JSON transformations and often runs slower than expected. The team wants to improve performance without changing the code. What should they try first?
⚠ Common exam trap
Many exam-takers assume performance issues must be solved by code optimization or architectural changes, overlooking that Lambda's memory setting directly controls CPU power, making it the simplest fix for CPU-bound functions.
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 memory setting
Increasing the Lambda memory setting allocates more CPU power proportionally, as AWS Lambda allocates CPU credits linearly with memory (up to 10,240 MB). For CPU-heavy JSON transformations, this directly reduces execution time without any code changes, making it the simplest and most effective first step.
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 Lambda memory setting
Why this is correct
Increasing the Lambda memory setting directly allocates more proportional vCPU power to the function. This is crucial for CPU-heavy JSON transformations because it provides the necessary computational resources to execute the intensive processing faster. By boosting the available vCPU, the function can complete its work more efficiently, directly addressing the performance constraint of slow execution without requiring any code modifications.
- ✗
Move the function to Amazon S3
Why it's wrong here
Amazon S3 is a massively scalable object storage service, not a compute runtime. Moving a Lambda function to S3 is technically meaningless because functions are executed by the Lambda service, and S3 can only store the code artifacts or trigger events, not process them. This action would not alter the allocation of vCPUs or memory to the function, so the CPU-bound JSON transformation would continue to perform identically.
When this WOULD be correct
A question asks how to reduce costs for infrequently accessed data that must be stored for compliance. The correct answer would be to move the data to Amazon S3 Glacier or S3 Standard-IA, not the function itself.
- ✗
Change the function to an ALB target
Why it's wrong here
Changing the function to an Application Load Balancer target would only change how requests are routed to the function, not the function's runtime resources. The ALB acts as an HTTP gateway, and while it can integrate with Lambda, it does not provide additional vCPUs, memory, or any compute acceleration. The function's CPU capacity is still dictated by its memory configuration, so this change would not resolve the slow CPU-heavy transformations.
When this WOULD be correct
When a question asks how to expose a Lambda function over HTTP/HTTPS with path-based routing or to integrate with an existing ALB, and the function is not already behind an API Gateway, then making it an ALB target would be correct.
- ✗
Disable CloudWatch logging
Why it's wrong here
Disabling CloudWatch logging would reduce the function's I/O overhead slightly by stopping log streams, but the bottleneck is CPU saturation during JSON transformation, not log flushing. CloudWatch Logs writes are asynchronous and consume minimal execution time, so eliminating them won't meaningfully increase CPU throughput. The performance issue would remain because CPU allocation is determined solely by the memory setting, not by logging configuration.
When this WOULD be correct
A Lambda function is experiencing timeouts due to excessive logging (e.g., logging large payloads in a tight loop). Disabling or reducing logging would free up execution time and prevent throttling, making it the correct first step.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The SAA-C03 exam frequently reuses these exact scenarios with slightly different constraints.
✓Increase the Lambda memory settingCorrect answer▾
Why this is correct
Increasing the Lambda memory setting directly allocates more proportional vCPU power to the function. This is crucial for CPU-heavy JSON transformations because it provides the necessary computational resources to execute the intensive processing faster. By boosting the available vCPU, the function can complete its work more efficiently, directly addressing the performance constraint of slow execution without requiring any code modifications.
✗Move the function to Amazon S3Wrong answer — click to see why▾
Why this is wrong here
Moving a Lambda function to Amazon S3 is not possible because S3 is a storage service, not a compute service. Lambda functions cannot be hosted or executed on S3.
★ When this WOULD be the correct answer
A question asks how to reduce costs for infrequently accessed data that must be stored for compliance. The correct answer would be to move the data to Amazon S3 Glacier or S3 Standard-IA, not the function itself.
Why candidates choose this
Candidates may confuse moving the function's code to S3 (e.g., storing deployment packages) with moving the function itself, or think S3 can execute code like Lambda.
✗Change the function to an ALB targetWrong answer — click to see why▾
Why this is wrong here
Changing the function to an ALB target does not improve CPU-heavy JSON transformation performance; it only changes how the function is invoked, not its execution resources.
★ When this WOULD be the correct answer
When a question asks how to expose a Lambda function over HTTP/HTTPS with path-based routing or to integrate with an existing ALB, and the function is not already behind an API Gateway, then making it an ALB target would be correct.
Why candidates choose this
Candidates may think that routing through an ALB can offload processing or improve performance, but ALB is a load balancer for HTTP traffic, not a compute optimizer.
✗Disable CloudWatch loggingWrong answer — click to see why▾
Why this is wrong here
Disabling CloudWatch logging does not improve CPU-bound performance; it only reduces logging overhead, which is negligible for CPU-heavy transformations.
★ When this WOULD be the correct answer
A Lambda function is experiencing timeouts due to excessive logging (e.g., logging large payloads in a tight loop). Disabling or reducing logging would free up execution time and prevent throttling, making it the correct first step.
Why candidates choose this
Candidates may think logging consumes significant resources and disabling it will speed up execution, but for CPU-heavy tasks, the bottleneck is compute, not I/O from logging.
Analysis generated from the official SAA-C03blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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 SAA-C03 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 SAA-C03 exam.