MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A startup wants to deploy a containerized ML application that includes both a model inference server and a preprocessing component in the same endpoint. Which SageMaker endpoint type supports running multiple containers?
⚠ Common exam trap
MLA-C01 often tests the confusion between multi-container endpoints (multiple containers, one endpoint) and multi-model endpoints (multiple models, one container) — candidates who conflate the two pick the wrong option.
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
SageMaker multi-container endpoints allow you to run up to 15 containers on a single endpoint, with containers invoked in a defined sequence (inference pipeline) or directly. This is the correct choice when a preprocessing component and a model inference server must coexist in one endpoint, since the preprocessing container can transform the request before passing it to the inference container. This pattern is ideal for encapsulating feature engineering with the 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.
- ✗
Asynchronous Inference
Why it's wrong here
Asynchronous Inference queues requests for large payloads and long processing, running one container per model. It is tempting as a containerised deployment mode, but it provides no multi-container composition; inference pipelines supply that chaining instead.
- ✓
Multi-container endpoint
Why this is correct
Multi-container endpoints run up to fifteen containers behind one endpoint, letting the inference server and preprocessing component be packaged and invoked together. This satisfies the stem's requirement to host both components within a single endpoint.
- ✗
Multi-model endpoint
Why it's wrong here
Multi-model endpoints load many models into one container to share hosting costs, not multiple distinct containers. It is tempting because the name suggests container multiplicity, but the axis is many models per container, whereas inference pipelines chain separate containers.
- ✗
Real-time endpoint
Why it's wrong here
Real-time endpoints run a single container per model; multi-container inference is delivered by SageMaker inference pipelines, which chain containers behind one endpoint. Real-time hosting is tempting because it is the default low-latency endpoint type, but it does not itself host multiple containers.
Go deeper
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.