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A company has a Microsoft Power Platform solution that contains the following components: * Microsoft Dataverse tables * A Microsoft Power Bl workspace named WS1 * A canvas app named App1 that uses Dataverse * A Power Bl semantic model that connects to Dataverse by using DirectQuery You plan to use generative Al to provide answers to queries based on a subset of corporate data. You need to ensure that the data is available as a grounding data source for Al systems. What should you do?
Correct Answer: D
The goal is to use generative AI to answer questions based on a subset of corporate data , and to ensure that this data is available as a grounding data source . The solution includes: * Dataverse tables * a Power BI workspace * a canvas app * a Power BI semantic model using DirectQuery to Dataverse The best action is D. Endorse the semantic model. Why D is correct: * Endorsing a semantic model makes it a trusted, discoverable enterprise data asset * It is the appropriate step when you want approved corporate data to be used reliably by downstream AI and analytics experiences * It fits the requirement of exposing a curated subset of data as a grounding source rather than duplicating or manually exporting it Why the other options are not correct: * A. Export the semantic model Exporting does not make it a governed grounding source. * B. Share WS1 Sharing the workspace grants access, but it does not establish the semantic model itself as the trusted data source for grounding. * C. Populate a Dataverse table The data already exists and is modeled through the semantic layer; creating another table is unnecessary for this requirement.
