Grounding Data Source for Generative AI in Power Platform

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Answer Correct answer: C — Endorse the semantic model to mark it as a trusted, high-quality, reusable data source for Copilot consumption.

A company has a Microsoft Power Platform solution that contains the following components: • Microsoft Dataverse tables • A Microsoft Power BI workspace named WS1 • A canvas app named App1 that uses Dataverse • A Power BI semantic model that connects to Dataverse by using DirectQuery You plan to use generative AI 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 AI systems. What should you do?

Populate a Dataverse table.
Share WS1.
Endorse the semantic model. Correct Answer
Export the semantic model.

Community Insight

The question tests the governance mechanism (Endorsement) required to expose existing Dataverse-backed data to AI systems, often confused with simply populating the underlying store.

To provide answers to queries based on corporate data using generative AI, you must ensure the data is available as a grounding data source. Endorsing the semantic model marks it as trusted and ready for Copilot consumption.

Candidates often select 'Populate a Dataverse table' because they focus only on the storage layer, missing that the AI system requires a specific governance signal (endorsement) to trust and utilize the semantic model.

Community Discussion (7 comments)

Shafz007 👍 1 Selected: A

Endorsing the semantic model marks it as promoted or certified, helping users identify trusted content and enabling discoverability. It does not itself configure the model as an AI grounding source. Microsoft Learn So A. Populate a Dataverse table remains the best answer. The required data subset can then be configured as a Dataverse knowledge source. My earlier explanation needed that distinction: populating the table prepares the data; adding it as a knowledge source enables grounding.

AxaptaKing1 👍 1 Selected: C

Answer in this case is C but Its important to understand, Semantic Model Endorsement is not grounding itself, but it is a very useful governance mechanism that helps AI systems choose and rely on trusted data sources for grounding.

kinser123 👍 1 Selected: C

Semantic Model Endorsement (Promoted or Certified): In Power BI and Fabric, endorsing a semantic model explicitly designates it as an authoritative, verified, and trusted dataset. Generative AI grounding mechanisms rely on endorsed/certified semantic models to ensure the AI retrieves validated corporate data rather than unverified sources.

VincentVega33 👍 1 Selected: C

since its either A or C - A. A -Populate a Dataverse table Simply storing data in Dataverse does not make it a grounding source for AI. The AI needs a supported, trusted data source such as an endorsed semantic model. Wheras C - Endorsing a semantic model (for example, marking it as Certified or Promoted) signals that it is a trusted, curated source of business data that AI can use to answer questions accurately.

omath 👍 2 Selected: A

The correct answer is: A. Populate a Dataverse table. To make data available as a grounding data source for generative AI systems (including Copilot Studio, Microsoft 365 Copilot, and other AI experiences), the data must live in a supported grounding store. From the options provided, the only store that AI systems can directly ground on is: Microsoft Dataverse The scenario already includes Dataverse, so the correct action is to populate a Dataverse table with the subset of corporate data you want to expose to AI.

deca0d4 👍 3 Selected: C

To endorse a semantic model means marking it as a trusted, high-quality, reusable data source in Power BI/Fabric.

hi1011 👍 3 Selected: C

"Consider using tags to label semantic models as ready for Copilot consumption. This method helps data consumers identify models that they can use with Copilot and expect better results. Alternatively, consider the readiness of a semantic model for use with Copilot as a criteria for its endorsement to promoted or even certified status." https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-semantic-models

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Expert Analysis

Why the Answer Is Correct

Endorsing the semantic model (Option C) is the correct action. In Microsoft Fabric and Power BI, endorsement (Promoted or Certified status) signals to AI systems like Microsoft Copilot that the model is a trusted, high-quality, reusable data source. This governance step is explicitly required for the model to be used as a grounding source.

Why the Other Options Are Wrong

Option A (Populate a Dataverse table) is incorrect because while Dataverse is the storage, the AI agent cannot automatically ground on unendorsed data. The prompt implies the solution already exists; the missing piece is the AI readiness configuration. Option B (Share WS1) grants access but does not establish the data's quality or trustworthiness for AI grounding. Option D (Export the semantic model) is irrelevant to exposing data to the AI service.

Community Comment Notes

Community consensus strongly supports Option C. As user 'hi1011' noted, Microsoft Learn documentation suggests using tags to label models as ready for Copilot consumption, which aligns with endorsement. User 'VincentVega33' clarified that while A provides the data, C provides the necessary trust signal for AI to use it.

Official Reference

https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-endorse-certify-models

Exam Strategy

When dealing with 'AI grounding' or 'Copilot' questions in the AB-100 exam, look for governance actions like 'Endorse', 'Certify', or 'Tag'. Do not assume that raw data availability (populating tables) is sufficient without the proper trust indicators.

Frequently Asked Questions

Why isn't populating the Dataverse table enough?

Dataverse stores the data, but the AI system needs an explicit governance signal (endorsement) to trust and query the semantic model.

What is the difference between Promoted and Certified?

Both are forms of endorsement that make a model usable for AI grounding. Certified implies higher rigor, but both satisfy the 'trusted source' requirement.

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Related Analysis

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