SAA-C03 Design High-Performing Architectures Practice Question
Exhibit
Lambda logs: REPORT RequestId: 9d6b... Duration: 184.27 ms Billed Duration: 185 ms Memory Size: 1024 MB Max Memory Used: 612 MB Init Duration: 812.43 ms Traffic pattern: - Low traffic outside weekdays 09:00-09:15 UTC - Predictable spike every weekday - Function language: Python 3.12 - No need to keep spare capacity all day
Based on the exhibit, a serverless checkout API is implemented in AWS Lambda and deployed in one Region. The function has a cold-start time of 700-900 ms on the first request after idle periods. Marketing launches a predictable traffic spike every weekday at 09:00 UTC, and the p95 latency target is under 150 ms during the first five minutes of the spike. What should the solutions architect do to meet the latency target while controlling cost?
⚠ Common exam trap
AWS often tests the distinction between provisioned concurrency (which pre-warms environments to eliminate cold starts) and reserved concurrency (which only caps the maximum concurrent executions without affecting cold-start behavior).
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
✓
Configure provisioned concurrency and scale it up before the predictable spike begins.
Provisioned concurrency pre-warms a specified number of execution environments so that the Lambda function has zero cold-start latency when invoked. By scheduling the provisioned concurrency to scale up before the 09:00 UTC spike, the function can serve the first requests within the 150 ms p95 latency target, while the scheduled scaling down after the spike controls cost by releasing unused capacity.
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 size and leave concurrency at the default value.
Why it's wrong here
Raising the function's memory setting also increases the allocated vCPU and can shorten execution time, but it does not alter Lambda's initialization lifecycle. After an idle period, Lambda tears down the sandbox and the next invocation must re-create the runtime and download the code, causing the same cold-start latency. The default concurrency limit only caps how many executions can run simultaneously; it does not pre-create environments, so increasing memory alone fails to reduce the predictable spike impact.
- ✓
Configure provisioned concurrency and scale it up before the predictable spike begins.
Why this is correct
Provisioned concurrency keeps a specified number of execution environments fully initialized and idle, ready to serve invocations in milliseconds instead of incurring a cold start. Because the spike is predictable, you can configure scheduled scaling to raise provisioned capacity to the expected peak before 09:00 UTC and lower it afterward, so users see low latency without paying idle costs all day. This is the only approach that directly eliminates the initialization delay by pre-warming the sandboxes.
- ✗
Put the Lambda function behind an Application Load Balancer so the load balancer absorbs the initialization delay.
Why it's wrong here
An Application Load Balancer is a layer-7 traffic distributor that simply forwards incoming requests to the Lambda function; it does not run code or pre-warm Lambda sandboxes. The first request after idle still triggers a cold start, and during that initialization the ALB merely waits (or eventually times out) while the function finishes spinning up. Adding an ALB in front only introduces another routing hop and is unable to absorb or mask the latency caused by creating a new execution environment.
- ✗
Set reserved concurrency to the expected peak so Lambda will pre-create execution environments.
Why it's wrong here
Reserved concurrency is an upper bound that caps the number of concurrent executions for a function, often used to isolate workloads and protect other functions from resource contention. It does not tell Lambda to create or maintain execution environments ahead of time, and Lambda may still scale from zero when the function has been idle. Setting reserved concurrency to the expected peak simply establishes a limit—it neither warms containers nor removes the initialization work, so it cannot eliminate cold-start latency.
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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