MLS-C01 Modeling Practice Question
A company is deploying a machine learning model using Amazon SageMaker. The model requires GPUs for inference. Which THREE configurations can the company use to meet this requirement? (Choose THREE.)
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
✓
Real-time endpoints with ml.p3 instance types
Real-time endpoints (option B) support GPU instances like ml.p3. Batch Transform (option C) also supports GPU instances. Elastic Inference (option E) provides GPU acceleration without a full GPU instance. Option A (SageMaker Serverless Inference) does not support GPU. Option D (SageMaker Studio) is an IDE, not an inference option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Serverless Inference
Why it's wrong here
SageMaker Serverless Inference does not support GPU instances, so it cannot meet the GPU requirement.
- ✓
Real-time endpoints with ml.p3 instance types
Why this is correct
Real-time endpoints with ml.p3 instance types provide full GPU support for inference.
- ✓
SageMaker Batch Transform with ml.p3 instances
Why this is correct
SageMaker Batch Transform with ml.p3 instances uses GPU for processing batch inference jobs.
- ✗
SageMaker Studio
Why it's wrong here
SageMaker Studio is an integrated development environment (IDE) and not an inference hosting option.
- ✓
SageMaker Elastic Inference (EI)
Why this is correct
SageMaker Elastic Inference allows attaching GPU acceleration to any instance type without needing a full GPU instance.
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.