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What query language does a Fabric data agent generate when an ontology is the data source?
GQL (Graph Query Language), because it traverses the graph structure of the ontology.
SQL, because the ontology is bound to lakehouse tables.
KQL, because the ontology includes an eventhouse data source.
When an ontology is the data source for a Fabric data agent, how do you improve the agent's accuracy?
By refining agent instructions, which guide how the agent interprets terminology, reasoning steps, response behavior, and scope.
By adding example query/question pairs to the data source configuration.
By modifying the ontology entity type definitions to match user phrasing.
A colleague receives a shared link for a published Fabric data agent backed by an ontology. They have the default (no extra) permission on the data agent link, but no permissions on the ontology item or its underlying lakehouse. What happens when they try to query the agent?
Their queries fail or return empty results, because they lack the required Read permissions on the ontology and its underlying data sources.
Their queries succeed, because the default permission grants full read access to all connected data sources.
They can only see the draft version of the agent, not the published version.
After publishing a Fabric data agent, what happens to the draft version?
The draft version remains editable, allowing you to continue refining the agent without affecting the published version that colleagues use.
The draft version is automatically deleted when you publish.
The draft version becomes read-only and can no longer be edited.
You must answer all questions before checking your work.
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