AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are preparing a dataset for fine-tuning an Azure OpenAI model in Azure AI Foundry. Which TWO data formatting and preparation requirements must you follow? (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
✓
Format training data as JSONL (JSON Lines) where each line is a chat completion example
Fine-tuning data for Azure OpenAI must be formatted as valid JSONL where each line represents a chat completion training example.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Include unencrypted passwords and PII in plain text to improve model empathy
Why it's wrong here
PII and sensitive data must be scrubbed before fine-tuning for privacy compliance.
- ✗
Upload raw unstructured PDF files directly as the fine-tuning training dataset
Why it's wrong here
PDF files cannot be used directly for model weight fine-tuning without prior parsing and formatting into JSONL.
- ✓
Format training data as JSONL (JSON Lines) where each line is a chat completion example
Why this is correct
Azure OpenAI fine-tuning requires JSONL format matching chat message structures.
- ✓
Ensure training examples follow the expected system, user, and assistant message roles
Why this is correct
Chat fine-tuning datasets must correctly use role tags to simulate conversational turns.
- ✗
Encode all training examples in binary audio wave files (.wav)
Why it's wrong here
Audio files are for speech-to-text models, not text fine-tuning.
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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.