- A
Use a SageMaker built-in PyTorch container as-is
Why wrong: SageMaker built-in PyTorch container as-is does not support custom inference logic without modification.
- B
Use SageMaker Ground Truth to deploy the model
Why wrong: SageMaker Ground Truth is for labeling, not for deploying models.
- C
Use SageMaker Processing to run inference
Why wrong: SageMaker Processing is designed for data processing, not for running inference.
- D
Create a custom inference script and use the SageMaker PyTorch container
Creating a custom inference script and using the SageMaker PyTorch container allows you to define custom processing logic for inference.
Question 1,413 of 1,755
MLS-C01 SageMaker PyTorch Container Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: sageMaker PyTorch Container. 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 machine learning engineer is deploying a PyTorch model to SageMaker. The model requires custom inference logic. Which approach should the engineer 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
✓
Create a custom inference script and use the SageMaker PyTorch container
Option D is correct because SageMaker allows you to provide a custom inference script (entry point) when using the PyTorch container, enabling custom inference logic. Option A is wrong because the built-in container as-is would not incorporate custom logic. Option B is wrong because SageMaker Ground Truth is for labeling, not model deployment. Option C is wrong because SageMaker Processing is for data processing, not inference.
Key principle: SageMaker PyTorch Container
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a SageMaker built-in PyTorch container as-is
Why it's wrong here
SageMaker built-in PyTorch container as-is does not support custom inference logic without modification.
- ✗
Use SageMaker Ground Truth to deploy the model
Why it's wrong here
SageMaker Ground Truth is for labeling, not for deploying models.
- ✗
Use SageMaker Processing to run inference
Why it's wrong here
SageMaker Processing is designed for data processing, not for running inference.
- ✓
Create a custom inference script and use the SageMaker PyTorch container
Why this is correct
Creating a custom inference script and using the SageMaker PyTorch container allows you to define custom processing logic for inference.
Related concept
SageMaker PyTorch Container
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- SageMaker PyTorch Container
- Custom Inference Script
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
SageMaker PyTorch Container
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. SageMaker PyTorch Container 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.
Review sageMaker PyTorch Container, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — SageMaker PyTorch Container.
What is the correct answer to this question?
The correct answer is: Create a custom inference script and use the SageMaker PyTorch container — Option D is correct because SageMaker allows you to provide a custom inference script (entry point) when using the PyTorch container, enabling custom inference logic. Option A is wrong because the built-in container as-is would not incorporate custom logic. Option B is wrong because SageMaker Ground Truth is for labeling, not model deployment. Option C is wrong because SageMaker Processing is for data processing, not inference.
What should I do if I get this MLS-C01 question wrong?
Review sageMaker PyTorch Container, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
SageMaker PyTorch Container
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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