Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
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You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.describe().show()
Does this meet the goal?

You have a Fabric tenant that contains JSON files in OneLake. The files have one billion items.
You plan to perform time series analysis of the items.
You need to transform the data, visualize the data to find insights, perform anomaly detection, and share the insights with other business users. The solution must meet the following requirements:
- Use parallel processing.
- Minimize the duplication of data.
- Minimize how long it takes to load the data.
What should you use to transform and visualize the data?

You need to create a data loading pattern for a Type 1 slowly changing dimension (SCD).
Which two actions should you include in the process? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

Which of the following features in the data profiling tools of Microsoft Fabric's Power Query Editor provides a set of visuals showcasing the frequency and distribution of the values in each column, sorted in descending order from the value with the highest frequency?

You have a Fabric tenant that uses a Microsoft Power BI Premium capacity.
You need to enable scale-out for a semantic model.
What should you do first?