AI-200 Containerized AI Workloads Practice Question
You are optimizing an AI model container image size. Which technique is most effective for reducing the footprint of an image based on a large Python deep learning framework?
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
✓
Implement multi-stage Docker builds.
Multi-stage builds allow you to keep only the runtime dependencies in the final image, excluding build-time tools.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement multi-stage Docker builds.
Why this is correct
This discards build-time compilers and source code, significantly shrinking the image.
- ✗
Enable ACR Georeplication.
Why it's wrong here
This improves availability, not image size.
- ✗
Store model weights as image layers.
Why it's wrong here
Weights should be externalized to Azure Blob Storage to keep images small.
- ✗
Use the 'latest' tag for all base images.
Why it's wrong here
Using 'latest' causes instability and does not reduce size.
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.