You have a Fabric workspace that contains a semantic model named Model1. You need to monitor the refresh history of Model1 and visualize the refresh history in a chart. What should you use?

The refresh history available directly in the semantic model's settings provides a tabular view of past refreshes, but it does not offer built-in charting capabilities.

A notebook, typically using Python or Spark, can leverage the Microsoft Fabric APIs or Power BI REST APIs to programmatically retrieve the refresh history data for a semantic model. Once the data is retrieved, the notebook can then use libraries like Matplotlib or Plotly to create custom charts and visualizations of the refresh history, meeting the requirement for visualization.

A DataFlow Gen2 dataflow is primarily used for ingesting and transforming data, not for monitoring and visualizing refresh history of a semantic model in a custom chart.

A data pipeline is used for orchestrating data movement and transformation activities, which is not the primary tool for custom visualization of semantic model refresh history.