Exam AB-100 Topic 2 Question 5 Discussion
Actual exam question for Microsoft's AB-100 exam
Question #: 5
Topic #: 2
Question #: 5
Topic #: 2
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?
* 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?
Suggested Answer: D Vote an answer
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.
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.
by Tom at Aug 21, 2026, 02:55 PM
0
0
0
10
Comments
Upvoting a comment with a selected answer will also increase the vote count towards that answer by one. So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.
Report Comment
Commenting
You can sign-up / login (it's free).