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. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. 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.summary() Does this meet the goal?

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A. Yes The `df.summary()` method computes various summary statistics for numeric columns (count, mean, stddev, min, max) and count for string columns, directly addressing the requirement to evaluate min, max, mean, and standard deviation. It provides a quick overview of the data's distribution across relevant columns.

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B. No The `df.summary()` method is specifically designed and commonly used for computing these types of descriptive statistics, making it an appropriate solution.

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 h

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