MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO AWS services can be used to deploy a machine learning model for serverless inference? (Choose 2.)
⚠ Common exam trap
A common mix-up: candidates confuse 'serverless' with any managed service (like ECS Fargate or AWS Batch) that abstracts servers, but only SageMaker Serverless Inference and AWS Lambda provide true pay-per-request, auto-scaling-to-zero inference without requiring you to manage compute resources or container orchestration.
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
✓
Amazon SageMaker Serverless Inference
Amazon SageMaker Serverless Inference automatically provisions, scales, and manages compute resources to run inference requests without requiring you to manage any underlying infrastructure. It scales down to zero when not in use and charges only for the compute time consumed, making it a fully serverless option for deploying ML models. AWS Lambda can also be used for serverless inference by packaging the model and inference code as a Lambda function. Lambda scales automatically, charges per invocation, and can be triggered by various AWS services, making it suitable for lightweight, event-driven inference workloads. Both services provide pay-per-request, auto-scaling-to-zero inference without requiring management of compute resources or container orchestration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker Serverless Inference
Why this is correct
Serverless inference option.
- ✓
AWS Lambda
Why this is correct
Lambda can host lightweight ML models.
- ✗
Amazon EMR
Why it's wrong here
EMR is for big data, not inference.
- ✗
Amazon ECS with Fargate
Why it's wrong here
ECS is not serverless by default; Fargate is serverless but not ML-specific.
- ✗
AWS Batch
Why it's wrong here
Batch is not serverless inference.
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.