AI-300 Genaiops Infrastructure Practice Question
Which Azure AI Foundry feature allows you to evaluate your model's performance?
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
✓
Evaluation
Evaluation in Azure AI Foundry allows you to run metrics against your model outputs to assess quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Endpoint monitoring
Why it's wrong here
Monitoring is for operational health, not output evaluation.
- ✗
Model Catalog
Why it's wrong here
Catalog is for deployment, not evaluation.
- ✗
Deployment logs
Why it's wrong here
Logs show execution results, not quality metrics.
- ✓
Evaluation
Why this is correct
This is the built-in feature for model assessment.
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.