AI-300 ML Model Lifecycle And Operations Practice Question
Your team needs to share a model across different workspaces. What is the most efficient way to achieve this in Azure Machine Learning?
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
✓
Use a shared Azure Machine Learning Registry.
Azure Machine Learning Registries allow for the sharing of model assets, environments, and components across multiple workspaces.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export the model as a pickle file and upload to each workspace.
Why it's wrong here
This is manual and error-prone; registries are designed for this.
- ✗
Duplicate the workspace storage account.
Why it's wrong here
This violates security and governance principles.
- ✗
Re-train the model in every workspace.
Why it's wrong here
This is inefficient and loses version lineage.
- ✓
Use a shared Azure Machine Learning Registry.
Why this is correct
Registries provide cross-workspace asset management.
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-300 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-300 exam.