- A
Use SageMaker Neo to compile the model for the target instance type
Why wrong: Neo compiles models for performance, but the error is about container support, not compilation.
- B
Request a service quota increase for the ml.p3.2xlarge instance type
Why wrong: Service quota is about limits, not container compatibility.
- C
Verify that the PyTorch framework version specified in the SageMaker estimator matches a version that supports GPU instances
Older PyTorch versions may not support GPU; using a supported version resolves the error.
- D
Create a custom inference container and use it with the SageMaker model
Why wrong: Custom container may still have the same issue; the error indicates the built-in container doesn't support the instance, so a different instance or framework version is needed.
Quick Answer
The correct action is to verify that the PyTorch framework version specified in the SageMaker estimator matches a version that supports GPU instances. This resolves the SageMaker PyTorch GPU instance compatibility error because SageMaker’s prebuilt PyTorch containers are compiled per version—only certain releases include CUDA and GPU libraries, so selecting a version without GPU support causes the container to reject GPU instance types like ml.p3.2xlarge. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of SageMaker’s framework version-to-instance mapping, a common trap where engineers assume all PyTorch versions work with GPU instances. Remember: not every PyTorch container is GPU-ready; always check the SageMaker documentation for version-specific GPU support. Memory tip: “GPU needs CUDA—if the container lacks it, the instance gets the axe.”
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 for real-time inference on SageMaker. The model requires GPU for low-latency predictions. The deployment fails with the error: 'The primary container does not support the requested instance type.' The instance type is ml.p3.2xlarge. Which action should the engineer take to resolve the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"primary"Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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
Verify that the PyTorch framework version specified in the SageMaker estimator matches a version that supports GPU instances
Option C is correct because the error 'The primary container does not support the requested instance type' typically occurs when the specified PyTorch framework version in the SageMaker estimator does not include GPU support for the chosen instance type (ml.p3.2xlarge). SageMaker's prebuilt PyTorch containers are version-specific and only certain versions are compiled with CUDA and GPU libraries; using a version that lacks GPU support causes the container to reject GPU instance types. Verifying and selecting a PyTorch version that explicitly supports GPU instances resolves the mismatch.
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.
- ✗
Use SageMaker Neo to compile the model for the target instance type
Why it's wrong here
Neo compiles models for performance, but the error is about container support, not compilation.
- ✗
Request a service quota increase for the ml.p3.2xlarge instance type
Why it's wrong here
Service quota is about limits, not container compatibility.
- ✓
Verify that the PyTorch framework version specified in the SageMaker estimator matches a version that supports GPU instances
Why this is correct
Older PyTorch versions may not support GPU; using a supported version resolves the error.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a custom inference container and use it with the SageMaker model
Why it's wrong here
Custom container may still have the same issue; the error indicates the built-in container doesn't support the instance, so a different instance or framework version is needed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume the error is due to resource limits (quota) or hardware incompatibility (Neo), rather than recognizing it as a framework version and container image mismatch specific to GPU support.
Detailed technical explanation
How to think about this question
SageMaker prebuilt PyTorch containers are tagged with framework versions (e.g., 'pytorch:1.13.1-gpu-py310') and each version has a specific set of supported instance families (CPU vs GPU). The container's entry point checks the instance type's accelerator capability via the NVIDIA Management Library (NVML) and fails if the container was not built with CUDA support. In real-world scenarios, using a CPU-only container tag (e.g., 'pytorch:1.13.1-cpu-py310') on a GPU instance triggers this exact error, while switching to the '-gpu' variant resolves it.
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: Verify that the PyTorch framework version specified in the SageMaker estimator matches a version that supports GPU instances — Option C is correct because the error 'The primary container does not support the requested instance type' typically occurs when the specified PyTorch framework version in the SageMaker estimator does not include GPU support for the chosen instance type (ml.p3.2xlarge). SageMaker's prebuilt PyTorch containers are version-specific and only certain versions are compiled with CUDA and GPU libraries; using a version that lacks GPU support causes the container to reject GPU instance types. Verifying and selecting a PyTorch version that explicitly supports GPU instances resolves the mismatch.
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.
Are there clue words in this question I should notice?
Yes — watch for: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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