In a Fabric notebook, which approach profiles the DataFrame with minimal administrative effort?

In a Fabric notebook, which approach profiles the DataFrame with minimal administrative effort?

• A. Create a pandas DataFrame first, and then visualize the data in an embedded Microsoft Power BI report by running QuickVisualize method on the pandas DataFrame.
• B. Display the DataFrame by running display(df), and then clicking the Inspect button.
• C. Display the DataFrame by running display(df), and then switching to chart view.
• D. Visualize the data in an embedded Microsoft Power BI report by running the QuickVisualize method on the DataFrame.

Answer: (B)

This item measures how to profile a DataFrame with the least setup in a Fabric notebook. The quickest path is to display the DataFrame and use the built-in Inspect option. After you run display(df), clicking Inspect opens a profiling view right in the notebook, showing data types, counts, missing values, basic statistics, and sample values without writing extra code or configuring external tools. It provides a concise, immediate snapshot of the data with just a couple of clicks. Other approaches add steps or require external tooling: QuickVisualize on the DataFrame or embedding Power BI involves extra calls or setup; switching to chart view is an additional UI action and focuses more on visualization than on quick profiling; and creating a separate DataFrame and then visualizing elsewhere introduces more overhead.

This item measures how to profile a DataFrame with the least setup in a Fabric notebook. The quickest path is to display the DataFrame and use the built-in Inspect option. After you run display(df), clicking Inspect opens a profiling view right in the notebook, showing data types, counts, missing values, basic statistics, and sample values without writing extra code or configuring external tools. It provides a concise, immediate snapshot of the data with just a couple of clicks.

Other approaches add steps or require external tooling: QuickVisualize on the DataFrame or embedding Power BI involves extra calls or setup; switching to chart view is an additional UI action and focuses more on visualization than on quick profiling; and creating a separate DataFrame and then visualizing elsewhere introduces more overhead.