AI-300 ML Model Lifecycle And Operations Practice Question
You want to perform hyperparameter tuning using the 'HyperDrive' service. You have a requirement to stop poor-performing runs early to save compute costs. Which policy should you use?
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
✓
'MedianStoppingPolicy'.
The 'BanditPolicy' is the standard early-termination policy that stops runs based on a slack factor/amount compared to the best-performing run.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
'MedianStoppingPolicy'.
Why this is correct
This is also valid, but Bandit is the most common answer for this scenario.
- ✓
'BanditPolicy'.
Why this is correct
Bandit is the classic implementation for this.
- ✗
'TruncationSelectionPolicy'.
Why it's wrong here
This is also valid.
- ✗
'RandomParameterSampling'.
Why it's wrong here
This is a sampling method, not a policy.
About these practice questions
This AI-300 question is part of Courseiva's 204-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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.