AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are configuring model fine-tuning for an Azure OpenAI model in Azure AI Foundry. You have prepared a JSONL training dataset. Which evaluation approach is recommended to verify that fine-tuning successfully improved model performance without causing catastrophic forgetting?
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
✓
Run an evaluation dataset comparing baseline and fine-tuned models using Azure AI Foundry evaluation metrics
Evaluating the fine-tuned model against a holdout test dataset using automated metrics (like GPT-4 as a judge) compares baseline vs. fine-tuned performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Inspect Azure Key Vault access audit logs
Why it's wrong here
Audit logs track secret access events, not machine learning model accuracy.
- ✗
Review Azure Cost Management invoices for training hours
Why it's wrong here
Cost invoices track financial spend, not qualitative model capabilities.
- ✓
Run an evaluation dataset comparing baseline and fine-tuned models using Azure AI Foundry evaluation metrics
Why this is correct
Comparing baseline and fine-tuned models on a holdout test dataset validates improvements and detects regressions.
- ✗
Check Azure Monitor CPU utilization percentages of the hosting cluster
Why it's wrong here
CPU metrics measure infrastructure resource usage, not model quality or task accuracy.
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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.