Quick Answer
The correct approach is to create a custom inference script and use the SageMaker PyTorch container. SageMaker’s PyTorch container is designed to accept an entry point script where you can define custom inference logic, including model loading, preprocessing, and postprocessing, by overriding the default `model_fn`, `input_fn`, `predict_fn`, and `output_fn` functions. This allows you to implement custom inference logic with PyTorch on SageMaker without managing your own infrastructure. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s deployment options and the distinction between built-in containers, which lack flexibility for arbitrary logic, and framework containers that support custom scripts. A common trap is confusing SageMaker Processing or Ground Truth for inference tasks—remember, Processing is for batch data transformation, not real-time predictions. Memory tip: think “entry point” for custom logic—if you need to bend the inference pipeline, you write a script, not pick a different service.
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. 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 machine learning engineer is deploying a PyTorch model to SageMaker. The model requires custom inference logic. Which approach should the engineer use?
Use a SageMaker built-in PyTorch container as-is
Why wrong: Built-in containers have fixed inference logic.
Use SageMaker Ground Truth to deploy the model
Why wrong: Ground Truth is for labeling.
Use SageMaker Processing to run inference
Why wrong: Processing is for batch processing, not real-time.
Create a custom inference script and use the SageMaker PyTorch container
SageMaker PyTorch container supports custom entry points.
Answer choices
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 B is correct because SageMaker allows you to provide a custom inference script (entry point) for PyTorch. Option A is wrong because SageMaker built-in containers do not support arbitrary custom logic. Option C is wrong because SageMaker Processing is for data processing, not inference. Option D is wrong because SageMaker Ground Truth is for labeling.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Built-in containers have fixed inference logic.
Use SageMaker Ground Truth to deploy the model
Why it's wrong here
Ground Truth is for labeling.
Use SageMaker Processing to run inference
Why it's wrong here
Processing is for batch processing, not real-time.
Create a custom inference script and use the SageMaker PyTorch container
Why this is correct
SageMaker PyTorch container supports custom entry points.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Create a custom inference script and use the SageMaker PyTorch container — Option B is correct because SageMaker allows you to provide a custom inference script (entry point) for PyTorch. Option A is wrong because SageMaker built-in containers do not support arbitrary custom logic. Option C is wrong because SageMaker Processing is for data processing, not inference. Option D is wrong because SageMaker Ground Truth is for labeling.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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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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