AI-102 Implement generative AI solutions Practice Question
A healthcare startup is developing a chatbot that uses Azure OpenAI to answer patient questions. They need to ensure that the chatbot only uses information from their verified medical database and does not generate unsupported medical advice. What is the best approach?
⚠ Common exam trap
Microsoft often tests the misconception that fine-tuning or content filtering alone can control factual accuracy, when in reality retrieval-augmented generation (RAG) with Azure AI Search is the correct pattern for grounding responses in specific, verified data.
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 AI Search with vector search to retrieve relevant documents and pass them as context.
It uses Azure AI Search with vector search to retrieve only relevant, verified documents from the medical database and passes them as context to the Azure OpenAI model. This grounds the model's responses in authoritative data, preventing it from generating unsupported medical advice. The retrieval-augmented generation (RAG) pattern ensures the chatbot answers are based on the provided context rather than the model's internal knowledge.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a model on the medical database and deploy it.
Why it's wrong here
Fine-tuning doesn't prevent the model from generating information outside the training data.
- ✗
Embed the entire medical database in the system message.
Why it's wrong here
Token limits prevent embedding large databases.
- ✗
Rely on Azure OpenAI's content filtering to block unsupported advice.
Why it's wrong here
Content filtering doesn't restrict to a specific data source.
- ✓
Use Azure AI Search with vector search to retrieve relevant documents and pass them as context.
Why this is correct
RAG ensures responses are grounded in indexed data.
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