Microsoft Azure AI Engineer Associate AI-102 Practice Question
You are building a custom agent in Azure AI Studio using the Azure AI Foundry Agent Service. The agent must cite private company manuals stored in an Azure Blob Storage container. You enable document indexing so the agent can retrieve relevant chunks at run time. Which Azure resource must you provision and attach to the agent to store and query the embeddings created during indexing?
An Azure App Configuration store
An Azure AI Search resource with vector search enabled
An Azure Cache for Redis instance configured for Search
Document indexing in the Azure AI Foundry Agent Service converts each file into vector embeddings that the agent can later search at inference time. The embeddings need to be stored in a retrieval-capable vector store. Azure AI Search provides a native vector index and REST APIs that the agent runtime knows how to query. Other services such as Azure Cosmos DB, Azure Cache for Redis, or Azure App Configuration do not integrate directly with the agent's memory system and therefore cannot be attached for this purpose.
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What are vector embeddings in Azure AI?
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