The correct option is option A, notebooks are used to manipulate data and write code, a pipeline does not directly store or visualize the update history of semantic models, and the data flow is to transform data.
To monitor the refresh history of a semantic model (such as Model1) in Microsoft Fabric (similar to Power BI models):
You can view the refresh history directly from the model's settings within the workspace.
This includes detailed logs such as refresh time, duration, status (success/failure), and error messages if any.
This is the most straightforward and built-in option for tracking model refreshes.
From there, if you need to visualize the refresh metrics (e.g., in a chart), you can export the refresh logs (manually or via API) and then load them into a report for visualization. -- ChatGPT
A. the refresh history from the settings of Model1
Option Reason
B. A notebook Not designed for refresh tracking; better for ad hoc analysis or transformation.
C. Dataflow Gen2 Used for data prep, not model refresh monitoring.
D. A data pipeline Helps trigger refreshes, but doesn’t track refresh history or show charts.
The correct answer is A, because Semantic models in Fabric (Power BI models) include a built-in refresh history feature accessible through their settings.
This view provides detailed logs of past refresh operations, including:
• Status (Succeeded/Failed)
• Duration
• Start and end times
You can export this history (e.g., to Excel or Power BI) to create charts and visualizations.
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