MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO AWS services can be used to deploy a trained model for serverless inference? (Select TWO.)
⚠ Common exam trap
Test-takers frequently confuse serverless inference with batch processing or managed container services, mistakenly selecting SageMaker batch transform or ECS with Fargate because they think 'serverless' means any managed service, but the key requirement is automatic scaling to zero and pay-per-request billing.
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
✓
AWS Lambda with a container image
AWS Lambda with a container image allows you to package a trained model and its dependencies into a Docker container and deploy it as a serverless function. Lambda automatically scales the inference endpoint in response to incoming requests, and you pay only for the compute time consumed during inference, with no idle infrastructure costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Lambda with a container image
Why this is correct
Serverless compute for small models.
- ✓
Amazon SageMaker Serverless Inference
Why this is correct
Serverless, auto-scaling.
- ✗
Amazon SageMaker batch transform
Why it's wrong here
Batch, not real-time serverless.
- ✗
Amazon Elastic Container Service (ECS) with Fargate
Why it's wrong here
Fargate is serverless but not primarily for ML inference.
- ✗
Amazon EC2 instances
Why it's wrong here
Not serverless.
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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Same concept, more angles
1 more way this is tested on MLS-C01
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Variation 1. Which TWO AWS services can be used to deploy a machine learning model for serverless inference? (Choose 2.)
easy- ✓ A.Amazon SageMaker Serverless Inference
- ✓ B.AWS Lambda
- C.Amazon EMR
- D.Amazon ECS with Fargate
- E.AWS Batch
Why A: 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.
JA
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.