- A
Use prompt engineering to add a system message that tells the model to only answer based on the uploaded PDFs. Keep the current deployment.
Why wrong: Prompt engineering alone does not guarantee the model will retrieve or adhere to the documents; it may still hallucinate.
- B
Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context.
This RAG approach grounds the model in the documents, reducing hallucinations and ensuring alignment with guidelines.
- C
Fine-tune GPT-4 on the medical documents using Azure OpenAI fine-tuning capabilities. Use the fine-tuned model for the chatbot.
Why wrong: Fine-tuning requires labeled data and may still produce hallucinations; it also requires retraining and is more complex.
- D
Deploy Azure AI Content Safety to filter responses that contradict guidelines. Set up a custom content filter using a list of approved phrases.
Why wrong: Content Safety filters harmful content, not factual accuracy; it cannot ensure alignment with specific documents.
Quick Answer
The correct choice is to index the medical PDFs into Azure Cognitive Search and configure the Azure OpenAI deployment to use 'Add your data' pointing to that index, with a system message instructing the model to base answers only on the retrieved context. This retrieval-augmented generation (RAG) approach grounds Azure OpenAI responses with custom data by pulling relevant passages from the indexed documents at inference time, ensuring alignment with official guidelines without retraining the model. On the AI-102 exam, this scenario tests your understanding of how to minimize hallucinations using Azure Cognitive Search as a vector or keyword index, with a common trap being to suggest fine-tuning instead—remember that RAG avoids retraining entirely. A useful memory tip: think of RAG as “retrieve first, generate second,” where the search index acts as a fact-checker for the model’s output.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 a data scientist at a healthcare company. You have deployed a GPT-4 model using Azure OpenAI to answer patient inquiries about medical conditions. The model is configured with temperature=0.3 and max_tokens=200. Recently, the compliance team flagged that some responses contain contradictory information compared to the official medical guidelines. You need to ensure the model's answers align strictly with the provided medical documents (stored as PDFs in Azure Blob Storage). You have access to Azure Cognitive Search and Azure AI Document Intelligence. The solution must minimize hallucinations and not require retraining the model. What should you do?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context.
Option B is correct because it uses Azure Cognitive Search to index the medical PDFs and then configures the Azure OpenAI deployment with 'Add your data' to retrieve relevant context from that index at inference time. This retrieval-augmented generation (RAG) approach grounds the model's answers in the official documents without retraining, directly addressing the compliance team's requirement to align responses with the provided guidelines and minimize hallucinations.
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.
- ✗
Use prompt engineering to add a system message that tells the model to only answer based on the uploaded PDFs. Keep the current deployment.
Why it's wrong here
Prompt engineering alone does not guarantee the model will retrieve or adhere to the documents; it may still hallucinate.
- ✓
Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context.
Why this is correct
This RAG approach grounds the model in the documents, reducing hallucinations and ensuring alignment with guidelines.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tune GPT-4 on the medical documents using Azure OpenAI fine-tuning capabilities. Use the fine-tuned model for the chatbot.
Why it's wrong here
Fine-tuning requires labeled data and may still produce hallucinations; it also requires retraining and is more complex.
- ✗
Deploy Azure AI Content Safety to filter responses that contradict guidelines. Set up a custom content filter using a list of approved phrases.
Why it's wrong here
Content Safety filters harmful content, not factual accuracy; it cannot ensure alignment with specific documents.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between prompt engineering (which is lightweight but unreliable for grounding) and RAG with a search index (which provides verifiable, document-grounded responses), leading candidates to choose the simpler prompt-only solution without considering its inability to enforce factual accuracy.
Detailed technical explanation
How to think about this question
Under the hood, 'Add your data' in Azure OpenAI works by sending the user query to Azure Cognitive Search, which performs a hybrid search (combining keyword and vector search) over the indexed PDF chunks, then injects the top-k results into the prompt as context before the model generates a response. This RAG pattern effectively constrains the model's output to the retrieved passages, reducing hallucination risk because the model is conditioned on factual snippets rather than relying solely on its parametric memory. In a real-world scenario, if a patient asks about a specific drug interaction, the search index would retrieve the relevant section from the medical PDF, and the model would generate an answer based solely on that text, even if its training data contained outdated or conflicting information.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Index the medical PDFs into Azure Cognitive Search. Configure the Azure OpenAI deployment to use 'Add your data' pointing to this index. Set the system message to instruct the model to base answers only on the retrieved context. — Option B is correct because it uses Azure Cognitive Search to index the medical PDFs and then configures the Azure OpenAI deployment with 'Add your data' to retrieve relevant context from that index at inference time. This retrieval-augmented generation (RAG) approach grounds the model's answers in the official documents without retraining, directly addressing the compliance team's requirement to align responses with the provided guidelines and minimize hallucinations.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Same concept, more angles
1 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 a senior AI engineer at a financial services company. You are building a generative AI solution to assist financial advisors with client portfolio recommendations. The solution must use Azure OpenAI Service. The following requirements must be met: 1. Responses must be based on the latest market data and client profiles stored in Azure SQL Database. 2. The solution must not generate investment advice that is not backed by the data. 3. The solution must be cost-effective and minimize API calls. 4. The system must provide citations for the data used in the response. You design a RAG pattern with Azure AI Search indexing the portfolio data. You also implement a system message instructing the model to only use provided context. However, the model occasionally generates advice that contradicts the data or invents new facts. You need to modify the solution to ensure responses are strictly grounded in the retrieved data. What should you do?
hard- ✓ A.Set temperature to 0 in the Azure OpenAI completion request.
- B.Reduce the top_k parameter in the search query to retrieve fewer documents.
- C.Increase the chunk size in the index to provide more context per document.
- D.Use Azure OpenAI Service on your own data integration to directly query the SQL database.
Why A: Option B is correct because setting temperature to 0 makes the model deterministic and less likely to invent. Option A is wrong because lowering top_k still allows creativity. Option C is wrong because adjusting chunk size affects retrieval but not the model's adherence. Option D is wrong because integrating with the data doesn't prevent hallucination if temperature is high.
Last reviewed: Jun 30, 2026
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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