MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data science team needs to deploy a PyTorch model that performs real-time inference with sub-100ms latency. The model requires GPU acceleration, but the team wants to minimize cost by sharing GPU instances across multiple models. Which SageMaker hosting option should they choose?
⚠ Common exam trap
Watch out — candidates often confuse SageMaker Serverless Inference with GPU support, but Serverless does not provide GPU acceleration, making it unsuitable for this latency-sensitive GPU workload.
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
✓
SageMaker real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance
SageMaker Multi-Model Endpoint (MME) allows multiple PyTorch models to share a single GPU instance (e.g., ml.g4dn), reducing cost while meeting sub-100ms latency requirements. MME dynamically loads and unloads models into GPU memory based on traffic, enabling real-time inference with GPU acceleration without dedicating a full instance per model.
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 real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance
Why this is correct
MME on GPU instances allows multiple models to share the same GPU, reducing cost while meeting latency requirements.
- ✗
SageMaker real-time endpoint with a single model per ml.g4dn instance
Why it's wrong here
This would not share the instance across multiple models, leading to higher cost for hosting many models.
- ✗
SageMaker Serverless Inference
Why it's wrong here
Serverless does not support GPU instances, so it cannot provide GPU acceleration.
- ✗
SageMaker Asynchronous Inference
Why it's wrong here
Asynchronous inference is designed for large payloads and does not guarantee sub-100ms latency.
Go deeper
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 835-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.