AI-200 Containerized AI Workloads Practice Question
You are deploying a custom PyTorch model to Azure Container Apps. You need to ensure that the container utilizes GPU acceleration. Which configuration step is required in the Container App environment?
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
✓
Assign a workload profile that supports GPU instances.
Azure Container Apps supports GPU-enabled nodes. You must select a workload profile that supports GPUs during the environment creation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the 'GPU-enabled' flag in the Container App resource settings.
Why it's wrong here
This setting does not exist in the resource configuration.
- ✓
Assign a workload profile that supports GPU instances.
Why this is correct
GPU support in ACA is managed through dedicated workload profiles.
- ✗
Set the --gpu-count parameter in the Azure CLI deploy command.
Why it's wrong here
This is not a valid parameter for ACA deployment.
- ✗
Install the NVIDIA CUDA driver manually inside the container image.
Why it's wrong here
Drivers are handled by the underlying infrastructure/base image, not manual installation in ACA.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-200 practice question is part of Courseiva's free Microsoft 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 AI-200 exam.