AI-103 Implement Generative AI And Agentic Solutions Practice Question
You are implementing a grounded RAG solution in Azure AI Foundry. Users report that the model occasionally hallucinates answers even when relevant documents are retrieved by Azure AI Search. What is the most effective prompt engineering strategy to mitigate this grounding 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
✓
Add strict prompt instructions stating that the model must rely solely on the retrieved context chunks and explicitly output a refusal statement if the answer is not present.
Instructing the model explicitly to answer using only the provided context and to state 'I don't know' when information is missing heavily reduces hallucinations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the system message entirely so the model can freely blend internal training weights with search results.
Why it's wrong here
Removing the system message removes crucial guardrails and behavioral controls, worsening hallucinations.
- ✓
Add strict prompt instructions stating that the model must rely solely on the retrieved context chunks and explicitly output a refusal statement if the answer is not present.
Why this is correct
Explicit grounding constraints in the system prompt force the model to adhere strictly to the retrieved context rather than its parametric memory.
- ✗
Increase the temperature parameter to 1.5 to encourage creative synthesis of the retrieved data.
Why it's wrong here
High temperature increases randomness and hallucination risk; temperature should be kept low (e.g., 0.0 to 0.3) for factual RAG.
- ✗
Disable chunking in Azure AI Search so the entire document is passed as a single massive token block.
Why it's wrong here
Passing entire documents without chunking exceeds context windows and degrades retrieval precision due to noise.
About these practice questions
Courseiva writes every AI-103 question from scratch — 510 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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-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.