easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An ML engineer needs to deploy a model as an AWS…
An ML engineer needs to deploy a model as an AWS Lambda function for serverless inference. The model is a scikit-learn pipeline serialized as a pickle file. What is the best way to include the model in the Lambda deployment?
⚠ Common exam trap
Many exam-takers think downloading from S3 on every invocation (Option C) is acceptable for serverless, but they overlook the severe cold-start latency and cost implications, or they confuse API Gateway's role as a proxy (Option B) without realizing it still needs a compute backend.
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
✓
Create a Lambda layer with the model file and use it in the function
Lambda layers allow you to package and include large dependencies, such as a serialized scikit-learn pipeline, separately from your function code. Layers are extracted into the /opt directory and are available across function invocations without cold-start overhead from downloading, making them the most efficient and best-practice approach for bundling static model artifacts in serverless inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a Lambda layer with the model file and use it in the function
Why this is correct
A layer allows the model to be included without increasing the function code size.
- ✗
Use API Gateway to proxy requests to the model stored in S3
Why it's wrong here
API Gateway is not a way to include the model in Lambda.
- ✗
Store the model in S3 and download it on every invocation
Why it's wrong here
Downloading on each invocation adds latency and cost.
- ✗
Mount an EFS file system containing the model
Why it's wrong here
EFS is for large models but adds complexity; not simplest for a small model.
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 MLA-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 MLA-C01 exam.