AI-300 Mlops Infrastructure Practice Question
Your Azure ML training job fails with an 'Out of Memory' error on a GPU cluster. You need to ensure the job runs successfully without modifying the model code. What should you do?
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
✓
Change the VM size to one with higher memory
Scaling up to a VM size with more RAM or GPU memory is the most direct way to resolve OOM issues in training jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the idle time for the compute cluster
Why it's wrong here
Idle time affects costs, not memory availability.
- ✗
Enable auto-scale to zero
Why it's wrong here
Auto-scaling helps with concurrency, not individual job memory constraints.
- ✓
Change the VM size to one with higher memory
Why this is correct
Selecting a VM SKU with more RAM and GPU memory resolves OOM errors.
- ✗
Update the workspace storage account
Why it's wrong here
Storage account updates do not impact compute memory limits.
Visual reference
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.