AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are evaluating generative AI model outputs in Azure AI Foundry. Which TWO automated evaluation metrics assess the quality and correctness of RAG responses against source data? (Choose TWO)
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
✓
Groundedness (measuring adherence to source context)
Groundedness and relevance are core Azure AI Foundry automated evaluation metrics used to assess RAG application 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.
- ✓
Groundedness (measuring adherence to source context)
Why this is correct
Groundedness evaluates whether model answers are supported by retrieved context.
- ✗
Virtual network gateway throughput
Why it's wrong here
Gateway throughput measures networking performance, not model output correctness.
- ✗
Storage account transaction count per hour
Why it's wrong here
Transaction counts measure storage activity, not AI text relevance.
- ✗
CPU utilization percentage of the Azure App Service plan
Why it's wrong here
CPU utilization monitors infrastructure performance, not RAG response quality.
- ✓
Relevance (measuring how well the response addresses the user query)
Why this is correct
Relevance evaluates whether the model directly answers the user's question without wandering off-topic.
About these practice questions
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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-103 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-103 exam.