Microsoft Azure AI Engineer Associate AI-102 Practice Question
While planning a generative AI project in Azure AI Foundry, you must let data scientists build prompt flows that reference confidential PDF files stored in an existing Azure Blob Storage container that is already cataloged in Microsoft Purview. To make the documents discoverable in the flow designer and preserve end-to-end data lineage, which configuration should you perform before anyone creates the first prompt flow?
Enable managed identity on the project's primary endpoint and grant it Storage Blob Data Reader on the container.
Create a dedicated Application Insights instance and link it to the project for telemetry.
Add the storage account as a deployment target in the hub's resource registry.
Register the container as a data connection in the project so it appears in the project's governed data catalog.
In Azure AI Foundry, data that prompt flows will consume must be surfaced to the project through a data connection. Creating a data connection that points to the Purview-governed storage container both exposes the files to the flow authoring experience and registers the asset with the project's lineage graph, allowing downstream tracking of which flows accessed the documents. Merely turning on managed identity, adding an Application Insights resource, or listing the storage account as a deployment target does not register the data asset or integrate it with Foundry's lineage capabilities.
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