MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is deploying a machine learning model using AWS Lambda for real-time inference. The model is a large ensemble model that takes approximately 500 MB of memory. The Lambda function is configured with 1024 MB of memory and a timeout of 15 seconds. The company observes that the function frequently times out during inference. The company wants to keep using Lambda for its serverless benefits. Which solution should the company implement to reduce inference time?
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 function memory to 3008 MB to provide more CPU resources.
Lambda has a maximum memory of 10,240 MB and a maximum timeout of 15 minutes. Increasing memory to 3008 MB gives more CPU power and reduces inference time. Option A is correct. Option B (SageMaker) moves away from serverless, which the company wants to keep. Option C (Step Functions) adds orchestration overhead and does not directly reduce inference time. Option D (ElastiCache) adds latency and cost and does not address the timeout issue.
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 function memory to 3008 MB to provide more CPU resources.
Why this is correct
Increasing memory to 3008 MB provides more CPU resources, reducing inference time.
- ✗
Deploy the model on Amazon SageMaker hosting instead of Lambda.
Why it's wrong here
Deploying on SageMaker moves away from serverless, which the company wants to keep.
- ✗
Use AWS Step Functions to invoke the Lambda function asynchronously.
Why it's wrong here
Using Step Functions adds orchestration overhead and does not directly reduce inference time.
- ✗
Use Amazon ElastiCache to cache model predictions and reduce computation.
Why it's wrong here
Using ElastiCache adds latency and cost, and does not address the timeout issue.
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 MLS-C01 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 MLS-C01 exam.