The correct answer is to implement Retrieval Augmented Generation (RAG) with your product and policy data. This approach grounds chatbot answers to specific data using RAG Azure OpenAI by retrieving only relevant documents from a vector database—such as Azure Cognitive Search—and injecting that context into the prompt, ensuring the model’s responses are strictly limited to your company’s information. When no matching documents are found for an off-topic query, the system naturally refuses to answer, creating a dynamic, data-driven boundary without modifying the underlying model. On the AI-102 exam, this tests your understanding of how to enforce content restrictions without fine-tuning, and a common trap is assuming you need to train a custom model or use system messages alone. Remember the memory tip: “No docs, no response”—if RAG retrieves nothing, the chatbot says nothing.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are building a customer support chatbot using Azure OpenAI Service. The chatbot must only answer questions related to the company's products and policies. It should refuse to answer off-topic questions. You need to implement this restriction effectively. What should you do?
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 your product and policy data
Option A is correct because Retrieval Augmented Generation (RAG) grounds the model's responses in your specific product and policy data by retrieving relevant documents from a vector database (e.g., Azure Cognitive Search) and injecting them into the prompt. This ensures the chatbot can only answer questions that have matching context in your data, and it naturally refuses off-topic queries because no relevant documents are retrieved, allowing the system to return a default refusal message. RAG provides a dynamic, data-driven boundary without modifying the underlying model.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 your product and policy data
Why this is correct
RAG grounds the model in your data, ensuring answers are only from that data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Fine-tune the model on a dataset of product-related conversations
Why it's wrong here
Fine-tuning can help but may still generate off-topic responses; RAG is more reliable.
✗
Use a system message that instructs the model to stay on topic
Why it's wrong here
System messages are guidelines and may not be strictly followed.
✗
Set the temperature parameter to 0 to reduce randomness
Why it's wrong here
Temperature does not enforce topic restrictions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that a system message or fine-tuning alone can reliably enforce content restrictions, when in practice RAG provides a grounded, data-driven boundary that prevents off-topic responses by design.
Detailed technical explanation
How to think about this question
Under the hood, RAG uses a retriever (e.g., Azure AI Search with semantic ranking) to convert the user query into an embedding, find the top-k most similar chunks from your indexed product/policy data, and prepend them as context in the prompt. The model then generates an answer solely from that context; if no relevant chunks are retrieved (e.g., for an off-topic query), the system can be programmed to output a refusal like 'I can only answer questions about our products and policies.' This approach leverages the model's in-context learning ability without altering its weights, making it easier to update data and maintain compliance.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement Retrieval Augmented Generation (RAG) with your product and policy data — Option A is correct because Retrieval Augmented Generation (RAG) grounds the model's responses in your specific product and policy data by retrieving relevant documents from a vector database (e.g., Azure Cognitive Search) and injecting them into the prompt. This ensures the chatbot can only answer questions that have matching context in your data, and it naturally refuses off-topic queries because no relevant documents are retrieved, allowing the system to return a default refusal message. RAG provides a dynamic, data-driven boundary without modifying the underlying model.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are building a customer support chatbot using Azure OpenAI Service. The chatbot must only respond based on the company's product documentation and should not generate answers outside that scope. Which approach should you use?
medium
A.Implement content filters to block responses not found in the documentation.
B.Fine-tune a GPT-4 model on the product documentation.
✓ C.Use Azure OpenAI On Your Data with a search index built from the documentation.
D.Use prompt engineering with a system message instructing the model to only answer from the documentation.
Why C: Option C is correct because Azure OpenAI On Your Data with a search index ensures the model only generates responses grounded in the provided documents. Option A is wrong because fine-tuning alone does not prevent the model from generating ungrounded content. Option B is wrong because prompt engineering may still result in hallucination. Option D is wrong because content filters block harmful content but do not enforce domain grounding.
Variation 2. Your organization is building a chatbot using Azure OpenAI Service. The chatbot must provide citations from a set of internal documents stored in Azure Blob Storage. You need to configure the solution to minimize token usage while ensuring citations are accurate. Which approach should you use?
medium
A.Embed all document content into the system prompt
B.Fine-tune a model on the documents so it can recall them from memory
C.Use a large context window model (e.g., 32K) and include all documents in the prompt
✓ D.Use Azure OpenAI on your data with Azure Cognitive Search for hybrid retrieval
Why D: Option B is correct because Azure OpenAI on your data with Azure Cognitive Search uses a hybrid retrieval approach (vector + keyword), which is more token-efficient than embedding all content into the prompt. Option A is wrong because embedding entire documents wastes tokens. Option C is wrong because it lacks retrieval. Option D is wrong because fine-tuning does not support dynamic document citation.
Last reviewed: Jun 30, 2026
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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.
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