AI-300 Practice Question: Generative AI Quality Assurance And Observability
You want to measure 'Relevance' in a RAG application. The relevance evaluator detects how well the response answers the user query. If the model provides a factually correct answer that does not address the prompt, which metric will capture this failure?
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
✓
Relevance
Relevance specifically measures if the response directly addresses the user's intent.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Relevance
Why this is correct
Relevance measures the alignment of the answer to the question.
- ✗
Fluency
Why it's wrong here
Fluency checks grammar and style.
- ✗
Coherence
Why it's wrong here
Coherence checks the flow of the answer.
- ✗
Groundedness
Why it's wrong here
Groundedness checks for hallucinations/source adherence.
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.