- A
Implement content filters to block responses not found in the documentation.
Why wrong: Content filters are designed to block offensive or harmful content, not to enforce domain-specific grounding.
- B
Fine-tune a GPT-4 model on the product documentation.
Why wrong: Fine-tuning adapts the model but does not guarantee responses are limited to the documentation; the model can still hallucinate.
- C
Use Azure OpenAI On Your Data with a search index built from the documentation.
This approach grounds the model on the indexed documents, ensuring responses are based on the documentation.
- D
Use prompt engineering with a system message instructing the model to only answer from the documentation.
Why wrong: Prompt engineering is insufficient to prevent hallucination; the model may still generate information outside the provided documents.
Grounding Chatbot Responses with Azure OpenAI On Your Data
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 respond based on the company's product documentation and should not generate answers outside that scope. Which approach should you use?
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 a search index built from the documentation.
Option C is correct because Azure OpenAI On Your Data allows you to ground the model's responses on a specific set of documents by connecting it to a search index (e.g., Azure Cognitive Search) built from the product documentation. This ensures the model retrieves relevant chunks from the index and generates answers solely based on that content, preventing out-of-scope responses 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 content filters to block responses not found in the documentation.
Why it's wrong here
Content filters are designed to block offensive or harmful content, not to enforce domain-specific grounding.
- ✗
Fine-tune a GPT-4 model on the product documentation.
Why it's wrong here
Fine-tuning adapts the model but does not guarantee responses are limited to the documentation; the model can still hallucinate.
- ✓
Use Azure OpenAI On Your Data with a search index built from the documentation.
Why this is correct
This approach grounds the model on the indexed documents, ensuring responses are based on the documentation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use prompt engineering with a system message instructing the model to only answer from the documentation.
Why it's wrong here
Prompt engineering is insufficient to prevent hallucination; the model may still generate information outside the provided documents.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse fine-tuning (Option B) with retrieval-augmented generation, assuming that training on documentation will fully constrain the model's output, when in fact fine-tuning does not prevent the model from generating information outside the training data due to its generative nature and lack of explicit retrieval grounding.
Detailed technical explanation
How to think about this question
Under the hood, Azure OpenAI On Your Data uses a Retrieval-Augmented Generation (RAG) pattern: when a user query is received, it is sent to a search index (e.g., Azure Cognitive Search) to retrieve the most relevant document chunks, which are then injected into the prompt as context for the GPT model. This approach ensures that the model's output is grounded in the retrieved data, significantly reducing hallucinations and keeping responses within the defined scope. In a real-world scenario, if a customer asks about a product feature not covered in the documentation, the system can be configured to respond with a fallback message like 'I cannot find that information in the available documentation' rather than fabricating an answer.
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
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FAQ
Questions learners often ask
What does this AI-102 question test?
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: Use Azure OpenAI On Your Data with a search index built from the documentation. — Option C is correct because Azure OpenAI On Your Data allows you to ground the model's responses on a specific set of documents by connecting it to a search index (e.g., Azure Cognitive Search) built from the product documentation. This ensures the model retrieves relevant chunks from the index and generates answers solely based on that content, preventing out-of-scope responses 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 →
Same concept, more angles
2 more ways this is tested on AI-102
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 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?
hard- ✓ A.Implement Retrieval Augmented Generation (RAG) with your product and policy data
- B.Fine-tune the model on a dataset of product-related conversations
- C.Use a system message that instructs the model to stay on topic
- D.Set the temperature parameter to 0 to reduce randomness
Why A: 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.
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 D is correct because Azure OpenAI on your data with Azure Cognitive Search for hybrid retrieval combines vector search and keyword search to efficiently find relevant document chunks from Azure Blob Storage, minimizing token usage by only sending the most pertinent content to the model for citation generation. This approach ensures accurate citations without embedding all documents into the prompt or relying on model memory.
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Last reviewed: Jul 4, 2026
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