- A
Build a custom container that extends the SageMaker PyTorch container and push it to Amazon ECR
Extending the container is a valid approach for additional dependencies.
- B
Include a requirements.txt file in the source directory
SageMaker reads requirements.txt and installs dependencies automatically.
- C
Use a lifecycle configuration to install dependencies
Why wrong: Lifecycle configurations are for notebook instances, not training jobs.
- D
Specify a custom Docker image in the PyTorch estimator
Why wrong: The PyTorch estimator uses its own container; specifying a custom image requires BYOC.
- E
Add the dependencies to the estimator's source_dir argument as a separate container
Why wrong: source_dir is for code, not for container.
Question 982 of 1,000
How to Add Custom Dependencies in SageMaker PyTorch Script Mode
This MLA-C01 practice question tests your understanding of ml model development. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: sageMaker Script Mode. 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 custom PyTorch model using SageMaker script mode. The training script requires specific dependencies not included in the default PyTorch container. Which TWO actions can the engineer take to ensure the dependencies are available? (Select TWO.)
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
✓
Build a custom container that extends the SageMaker PyTorch container and push it to Amazon ECR
Option A is correct: Building a custom container that extends the SageMaker PyTorch container and pushing it to Amazon ECR allows you to include any dependencies not available in the default container. Option B is correct: Including a requirements.txt file in the source directory causes SageMaker to automatically install those dependencies during training. Option C is incorrect: Lifecycle configurations are only applicable to notebook instances, not training jobs. Option D is incorrect: While you can specify a custom Docker image using the image_uri parameter, the action of building the container is covered by option A; option D is not a separate valid action to ensure dependencies. Option E is incorrect: The source_dir argument is for pointing to a directory containing training code, not for specifying a separate container.
Key principle: SageMaker Script Mode
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Build a custom container that extends the SageMaker PyTorch container and push it to Amazon ECR
Why this is correct
Extending the container is a valid approach for additional dependencies.
Related concept
SageMaker Script Mode
- ✓
Include a requirements.txt file in the source directory
Why this is correct
SageMaker reads requirements.txt and installs dependencies automatically.
Related concept
SageMaker Script Mode
- ✗
Use a lifecycle configuration to install dependencies
Why it's wrong here
Lifecycle configurations are for notebook instances, not training jobs.
- ✗
Specify a custom Docker image in the PyTorch estimator
Why it's wrong here
The PyTorch estimator uses its own container; specifying a custom image requires BYOC.
- ✗
Add the dependencies to the estimator's source_dir argument as a separate container
Why it's wrong here
source_dir is for code, not for 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 Script Mode
- Custom Container for SageMaker
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 Script Mode
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 Script Mode 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 Script Mode, then practise related MLA-C01 questions on the same topic to reinforce the concept.
- →
ML Model Development — study guide chapter
Learn the concepts, then practise the questions
- →
ML Model Development practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
1,000 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Model Development — This question tests ML Model Development — SageMaker Script Mode.
What is the correct answer to this question?
The correct answer is: Build a custom container that extends the SageMaker PyTorch container and push it to Amazon ECR — Option A is correct: Building a custom container that extends the SageMaker PyTorch container and pushing it to Amazon ECR allows you to include any dependencies not available in the default container. Option B is correct: Including a requirements.txt file in the source directory causes SageMaker to automatically install those dependencies during training. Option C is incorrect: Lifecycle configurations are only applicable to notebook instances, not training jobs. Option D is incorrect: While you can specify a custom Docker image using the image_uri parameter, the action of building the container is covered by option A; option D is not a separate valid action to ensure dependencies. Option E is incorrect: The source_dir argument is for pointing to a directory containing training code, not for specifying a separate container.
What should I do if I get this MLA-C01 question wrong?
Review sageMaker Script Mode, then practise related MLA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
SageMaker Script Mode
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.