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Microsoft Azure AI Engineer Associate AI-102 Practice Question

You are designing a Retrieval-Augmented Generation (RAG) solution in Azure AI Foundry. Your project will ground GPT-35-Turbo responses on 30 000 PDF pages stored in Azure Blob Storage. You create a data connection, enable automatic chunking, and plan to build a vector index so the prompt flow can retrieve the most relevant passages at runtime. Which Foundry resource must you create to store the embeddings so they can be queried by the prompt flow retrieval node?

  • A semantic memory collection in Azure Cosmos DB for NoSQL

  • An Azure Cache for Redis instance with the Search and Query modules enabled

  • A feature store table in Azure Machine Learning

  • A vector index backed by Azure AI Search

Microsoft Azure AI Engineer Associate AI-102
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