- A
Create a custom Docker container and push to ECR.
Why wrong: Custom containers are needed for non-standard environments, but PyTorch is already supported.
- B
Launch a SageMaker notebook instance, install dependencies, and run training script.
Why wrong: Notebook instances are for development, not for one-time training jobs.
- C
Use the SageMaker PyTorch estimator with a pre-built container.
The framework estimator manages the container and allows adding custom dependencies via source_dir.
- D
Use the SageMaker generic container and install PyTorch via a lifecycle configuration.
Why wrong: The generic container requires manual setup and is less efficient.
Question 478 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 data scientist needs to run a one-time training job on a large dataset using SageMaker. The job requires a specific PyTorch version and custom dependencies. Which approach is MOST efficient?
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
✓
Use the SageMaker PyTorch estimator with a pre-built container.
Option C is correct because the SageMaker PyTorch estimator provides a pre-built, optimized container with the specified PyTorch version, eliminating the need to manage custom Docker images or manual dependency installation. For a one-time training job, this approach is the most efficient as it requires minimal setup and leverages SageMaker's managed infrastructure for training.
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.
- ✗
Create a custom Docker container and push to ECR.
Why it's wrong here
Custom containers are needed for non-standard environments, but PyTorch is already supported.
- ✗
Launch a SageMaker notebook instance, install dependencies, and run training script.
Why it's wrong here
Notebook instances are for development, not for one-time training jobs.
- ✓
Use the SageMaker PyTorch estimator with a pre-built container.
Why this is correct
The framework estimator manages the container and allows adding custom dependencies via source_dir.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the SageMaker generic container and install PyTorch via a lifecycle configuration.
Why it's wrong here
The generic container requires manual setup and is less efficient.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the distinction between using a fully managed estimator (like PyTorch) versus manual containerization or notebook-based training, where candidates may overcomplicate the solution by choosing custom Docker (Option A) due to familiarity with containerization, missing that pre-built containers are more efficient for standard frameworks.
Detailed technical explanation
How to think about this question
The SageMaker PyTorch estimator automatically provisions the training infrastructure, downloads the pre-built Docker image from Amazon ECR (e.g., 763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:<tag>), and handles hyperparameter tuning, checkpointing, and distributed training if needed. Under the hood, it uses the SageMaker Training Toolkit to manage the training loop, and custom dependencies can be added via a requirements.txt file or source_dir parameter, which SageMaker installs in the container environment without requiring a custom image. This approach is ideal for one-time jobs because it abstracts infrastructure management and optimizes for cost with spot instance support.
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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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the SageMaker PyTorch estimator with a pre-built container. — Option C is correct because the SageMaker PyTorch estimator provides a pre-built, optimized container with the specified PyTorch version, eliminating the need to manage custom Docker images or manual dependency installation. For a one-time training job, this approach is the most efficient as it requires minimal setup and leverages SageMaker's managed infrastructure for training.
What should I do if I get this MLS-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 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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