AI-102 Implement generative AI solutions Practice Question
You are building a generative AI solution using Azure OpenAI Service. The application must retrieve information from a large private knowledge base. You need to ensure the model uses only relevant documents from the knowledge base to generate answers. Which feature should you configure?
⚠ Common exam trap
Many candidates confuse fine-tuning (D) with retrieval-augmented generation (RAG), assuming that training the model on the knowledge base is the best way to ground answers, when in fact RAG with vector search is the correct pattern for dynamic, relevant document retrieval without modifying the base model.
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
✓
Use Azure OpenAI On Your Data with vector search
B is correct because Azure OpenAI On Your Data with vector search enables the model to retrieve only the most semantically relevant documents from a private knowledge base by converting both the user query and the documents into high-dimensional vectors and performing similarity search. This ensures the model's responses are grounded in the specific, relevant information without exposing the entire knowledge base to the model.
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 a custom prompt flow
Why it's wrong here
Prompt flow orchestrates LLM, Python and tool steps but supplies no document index or retrieval mechanism by itself. It tempts because flows commonly wire retrieval into a pipeline, yet the actual grounding requires Azure AI Search configured as the data source.
- ✓
Use Azure OpenAI On Your Data with vector search
Why this is correct
Azure OpenAI On Your Data with vector search indexes the private knowledge base and retrieves semantically relevant chunks, grounding responses in those documents rather than the model's parametric knowledge. This constrains generation to the supplied content.
- ✗
Configure a content filter
Why it's wrong here
Content filters screen harmful categories in prompts and completions; they perform no retrieval from a knowledge base. It tempts because filtering sounds like restricting output, but relevance to private documents requires an index and retrieval step, not safety classification.
- ✗
Fine-tune the model with the knowledge base
Why it's wrong here
Fine-tuning bakes knowledge into model weights and cannot restrict answers to retrieved documents at query time. It tempts because fine-tuning does adapt a model to a domain, but the requirement for grounding on relevant documents is met by Azure AI Search retrieval with the On Your Data feature.
Go deeper
Related to this question
About these practice questions
This AI-102 question is part of Courseiva's 761-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.