- A
Multi-model endpoint
Why wrong: Multi-model endpoints serve multiple models within the same framework container, not different frameworks.
- B
Real-time endpoint with a single container
Why wrong: A single container cannot run multiple frameworks simultaneously.
- C
Multi-container endpoint
Multi-container endpoints support multiple inference containers, each with its own environment.
- D
Asynchronous endpoint
Why wrong: Asynchronous endpoints are for large payloads and long processing times, not for multi-container setups.
Question 407 of 1,000
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding 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.
- ✗
Multi-model endpoint
Why it's wrong here
Multi-model endpoints serve multiple models within the same framework container, not different frameworks.
- ✗
Real-time endpoint with a single container
Why it's wrong here
A single container cannot run multiple frameworks simultaneously.
- ✓
Multi-container endpoint
Why this is correct
Multi-container endpoints support multiple inference containers, each with its own environment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Asynchronous endpoint
Why it's wrong here
Asynchronous endpoints are for large payloads and long processing times, not for multi-container setups.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker multi-container endpoints use a proxy container (e.g., NGINX or TensorFlow Serving) to route requests to the appropriate inference container based on a defined invocation pattern (serial or direct). In a real-world scenario, you might deploy a pre-processing container (e.g., for feature engineering) followed by a model container, all within the same endpoint, reducing latency compared to chaining separate endpoints. The endpoint's InvokeEndpoint API can target specific containers using the TargetContainerHeader, enabling fine-grained control over the inference pipeline.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Deployment and Orchestration of ML Workflows — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Multi-container endpoint — Option C is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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