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AI-200 Containerized AI Workloads Practice Question

You are deploying a custom PyTorch inference model container to Azure Container Apps. The application requires GPU acceleration for real-time video processing. Which Azure Container Apps environment configuration tier must you select to support this requirement?

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

Dedicated workload profiles (Consumption and Dedicated)

GPU-enabled nodes in Azure Container Apps are supported exclusively through the Consumption dedicated workload profiles tier, allowing you to scale container apps with NVIDIA GPU hardware.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Serverless container instances tier

    Why it's wrong here

    Azure Container Instances supports GPUs, but Azure Container Apps specifically utilizes workload profiles for GPU allocation.

  • Azure Container Apps Basic tier

    Why it's wrong here

    Azure Container Apps does not have a Basic tier; it uses a consumption-based or workload profile architecture.

  • Dedicated workload profiles (Consumption and Dedicated)

    Why this is correct

    GPU-enabled container apps require dedicated workload profiles that support NC-series or similar GPU nodes.

  • Consumption standard workload profile

    Why it's wrong here

    The standard Consumption profile uses CPU-only nodes and does not provision GPU hardware.

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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

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