AI-102 Implement generative AI solutions Practice Question
You are building a generative AI application using Azure OpenAI Service. The application must provide factual answers based on your company's internal knowledge base. You need to minimize the risk of the model generating incorrect information (hallucinations). Which approach should you take?
⚠ Common exam trap
The AI-102 exam often tests the misconception that fine-tuning (Option B) is the best way to ground a model in proprietary data, but the trap is that fine-tuning does not provide dynamic, query-specific retrieval and can still produce hallucinations, whereas RAG explicitly forces the model to use retrieved facts.
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
✓
Implement Retrieval-Augmented Generation (RAG) with Azure AI Search.
Retrieval-Augmented Generation (RAG) with Azure AI Search grounds the model's responses in your company's internal knowledge base by retrieving relevant documents in real time and injecting them into the prompt. This reduces hallucinations by ensuring the model generates answers based on retrieved facts rather than relying solely on its parametric memory. Azure AI Search provides vector and hybrid search capabilities that efficiently index and query your documents, making RAG the most effective approach for factual 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.
- ✓
Implement Retrieval-Augmented Generation (RAG) with Azure AI Search.
Why this is correct
RAG retrieves relevant documents and uses them as context, reducing hallucinations.
- ✗
Fine-tune the model on your company's documents.
Why it's wrong here
Fine-tuning improves relevance but can still hallucinate.
- ✗
Use few-shot prompting with examples of correct answers.
Why it's wrong here
Few-shot prompting helps but does not prevent hallucinations.
- ✗
Set the max_tokens parameter to a low value.
Why it's wrong here
max_tokens limits length, not factual accuracy.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 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-102 exam.