hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is deploying a…
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?
⚠ Common exam trap
Watch out — 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.
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
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.
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.
- ✗
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.
Visual reference
Go deeper
Related to this question
About these practice questions
One of 835 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.