AI-102 Plan and manage an Azure AI solution Practice Question
Your organization deploys an Azure AI Foundry solution for a customer service chatbot. The chatbot uses a large language model (LLM) hosted on Azure OpenAI Service with a GPT-4 model. Requirements: (1) The chatbot must only use information from the company's internal knowledge base, not general internet knowledge. (2) Responses must include citations from the knowledge base. (3) The solution must filter out any toxic or harmful content. (4) The chatbot must be deployed in a secure environment with network isolation. You have an Azure AI Foundry project with a connected Azure OpenAI resource. The knowledge base is stored in Azure AI Search. You need to configure the solution. What should you do?
⚠ Common exam trap
Many exam-takers confuse fine-tuning (Option D) with RAG, not realizing that fine-tuning cannot provide citations and still risks hallucination, while RAG with Azure AI Search directly satisfies the grounding and citation requirements.
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 Azure AI Search as the data source, enable content filtering, and configure the model to use the search index with strict grounding.
It uses Azure OpenAI on your data with Azure AI Search as the data source, which ensures the model only retrieves and generates responses from the indexed knowledge base, meeting the requirement to avoid general internet knowledge. Enabling content filtering satisfies the toxicity requirement, and configuring strict grounding ensures responses include citations from the search index. The secure environment with network isolation is achieved through Azure AI Foundry's managed network capabilities, which are compatible with this configuration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure OpenAI with function calling to retrieve knowledge base documents, and enable content filtering.
Why it's wrong here
Function calling requires custom code and does not enforce citations automatically.
- ✗
Use prompt engineering with system messages to restrict knowledge, and enable content filtering.
Why it's wrong here
Prompt engineering is not reliable; the model may still produce ungrounded responses.
- ✓
Use Azure OpenAI on your data with Azure AI Search as the data source, enable content filtering, and configure the model to use the search index with strict grounding.
Why this is correct
Azure OpenAI on your data grounds responses in the knowledge base, content filtering ensures safety, and strict grounding enforces citations.
- ✗
Fine-tune the GPT-4 model on the knowledge base and deploy with content filtering.
Why it's wrong here
Fine-tuning does not guarantee responses are limited to the knowledge base; it may still hallucinate.
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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.