AI-300 ML Model Lifecycle And Operations Practice Question
You are defining an Azure Machine Learning environment for a training job. The environment requires a specific set of Python libraries. What is the best practice for defining these dependencies?
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
✓
Define dependencies in a 'conda.yaml' file.
Using a 'conda.yaml' file is the best practice for managing reproducible Python environments in Azure ML.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define dependencies in a 'conda.yaml' file.
Why this is correct
This ensures consistent environment creation.
- ✗
Hardcode pip install commands in the training script.
Why it's wrong here
This creates hidden dependencies and is poor practice.
- ✗
Install libraries via a startup script in the compute cluster.
Why it's wrong here
This is inefficient and slow for repeated runs.
- ✗
Pre-install them on the virtual machine image.
Why it's wrong here
Images are managed by the platform; you should use environment files.
About these practice questions
One of 204 original AI-300 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 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.