- A
Triton Inference Server on SageMaker
Why wrong: Triton can serve multiple models from different frameworks within one container, but the question asks for multiple containers.
- B
Multi-container endpoint
Multi-container endpoints allow up to 15 containers, each with its own framework, running on the same instance.
- C
Separate endpoints for each framework
Why wrong: Separate endpoints increase cost and complexity; multi-container endpoints unify them.
- D
Multi-model endpoint (MME)
Why wrong: MME hosts multiple models from the same framework in a single container; it does not support different frameworks natively.
Question 958 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 wants to deploy a PyTorch model that uses dynamic batching and model ensemble. They need to serve multiple models with different frameworks (PyTorch, TensorFlow) within the same endpoint. Which SageMaker feature should they use?
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
B is correct because a multi-container endpoint allows you to run multiple containers (e.g., one for PyTorch, one for TensorFlow) within the same SageMaker endpoint, enabling model ensemble and dynamic batching across different frameworks. This feature supports serving models with heterogeneous frameworks and dependencies without needing separate endpoints, while still providing a single inference endpoint for clients.
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.
- ✗
Triton Inference Server on SageMaker
Why it's wrong here
Triton can serve multiple models from different frameworks within one container, but the question asks for multiple containers.
- ✓
Multi-container endpoint
Why this is correct
Multi-container endpoints allow up to 15 containers, each with its own framework, running on the same instance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Separate endpoints for each framework
Why it's wrong here
Separate endpoints increase cost and complexity; multi-container endpoints unify them.
- ✗
Multi-model endpoint (MME)
Why it's wrong here
MME hosts multiple models from the same framework in a single container; it does not support different frameworks natively.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between multi-model endpoints (which share a container) and multi-container endpoints (which run separate containers), so the trap here is assuming that MME can handle different frameworks, when in fact it requires all models to be compatible with the same container environment.
Detailed technical explanation
How to think about this question
Under the hood, a multi-container endpoint uses SageMaker's inference pipeline with multiple containers behind a single endpoint, where each container can run its own inference server (e.g., TorchServe for PyTorch, TFServing for TensorFlow). The endpoint routes requests to the appropriate container based on the model name or a custom logic, enabling ensemble predictions by aggregating outputs from multiple containers. In a real-world scenario, this is critical for A/B testing or gradual migration from TensorFlow to PyTorch without rewriting the serving infrastructure.
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 — B is correct because a multi-container endpoint allows you to run multiple containers (e.g., one for PyTorch, one for TensorFlow) within the same SageMaker endpoint, enabling model ensemble and dynamic batching across different frameworks. This feature supports serving models with heterogeneous frameworks and dependencies without needing separate endpoints, while still providing a single inference endpoint for clients.
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.
About these practice questions
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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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