AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are designing a generative AI application in Azure AI Foundry and want to categorize user queries into predefined intent classes before routing them to specific agents. Which prompt engineering technique should you apply?
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
✓
Few-shot prompting with labeled classification examples
Few-shot classification prompting provides examples of inputs paired with their corresponding categories to guide the model's classification accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Zero-shot random generation
Why it's wrong here
Zero-shot without examples is less reliable for specific classification taxonomies.
- ✗
Enabling JSON output mode without schema definition
Why it's wrong here
JSON mode ensures valid syntax but does not provide classification examples.
- ✓
Few-shot prompting with labeled classification examples
Why this is correct
Providing input-output classification examples in the prompt teaches the model how to categorize incoming queries.
- ✗
Increasing model temperature to 1.5
Why it's wrong here
High temperature increases randomness and degrades classification consistency.
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.