MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company needs to serve real-time predictions from a large ensemble of three deep learning models, each requiring different inference environments (PyTorch, TensorFlow, MXNet). Which SageMaker endpoint type supports running multiple inference containers together?
⚠ Common exam trap
It's easy for candidates to confuse 'multi-model endpoint' (multiple models in one container) with 'multi-container endpoint' (multiple containers with different environments), leading them to incorrectly select Option A.
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
✓
Multi-container endpoint
Amazon SageMaker multi-container endpoints allow you to run multiple inference containers (e.g., PyTorch, TensorFlow, MXNet) within a single endpoint, each handling different models or inference environments. This is achieved by deploying multiple containers behind a single endpoint with a serial or direct invocation pattern, enabling real-time predictions from the ensemble without managing separate endpoints.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Multi-model endpoint
Why it's wrong here
A multi-model endpoint hosts many models behind one container, so it cannot run three distinct inference stacks; the models must share a single framework and image. It is tempting because it serves multiple models, and would be correct for hosting many same-framework models cost-effectively behind one endpoint.
- ✗
Real-time endpoint with a single container
Why it's wrong here
A single-container real-time endpoint runs one image, so it cannot host PyTorch, TensorFlow and MXNet together. It is tempting because real-time endpoints give the low-latency responses the ensemble needs, and would be correct if all three models shared one framework and image.
- ✓
Multi-container endpoint
Why this is correct
Multi-container endpoints run up to fifteen containers together on one instance, letting each model use its own PyTorch, TensorFlow or MXNet environment. This satisfies the stem's need for multiple inference containers co-hosted, which single-model and serverless options cannot provide.
- ✗
Asynchronous endpoint
Why it's wrong here
An asynchronous endpoint queues requests and returns results via Amazon S3, so it cannot return real-time predictions, and it still runs a single container. It is tempting because it suits large payloads and long processing times, and would be correct for batch-style inference where immediate responses are unnecessary.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 665 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.