Microsoft Azure AI Engineer Associate AI-102 Practice Question
In an Azure AI Studio project, you plan to answer employee questions about 10,000 internal PDF manuals stored in Azure Blob Storage. You must minimize hallucinations, include source citations, and avoid retraining the model. Which approach should you implement before calling the gpt-35-turbo deployment from your prompt flow?
Create a data source connection that indexes the PDFs in Azure AI Search with semantic vector search, then attach the connection to an Index Lookup tool in your prompt flow so the model receives the top passages at runtime.
Call the chat completion endpoint with temperature set to 0 and a higher max_tokens value so the model can recall more facts.
Fine-tune gpt-35-turbo on the extracted text of the PDFs and deploy the custom model in the project.
Enable content safety filters and define a strict user content policy in the system message to reduce hallucinations.
Retrieval-augmented generation (RAG) is the recommended pattern for grounding a large language model in enterprise data. By indexing the PDF content in Azure AI Search with semantic vector search and connecting that index to an Index Lookup tool in the prompt flow, the platform passes the top-ranked passages and their metadata to the gpt-35-turbo chat completion. This allows the model to generate answers based strictly on retrieved passages and surface the citations. Fine-tuning would require additional cost and still risks stale knowledge, while merely lowering temperature, increasing tokens, or enabling content filters does not provide document grounding and therefore cannot reliably prevent hallucinations or supply citations.
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