Answer-first summary for fast verification
Answer: No
The solution does not meet the goal because df.explain() is used to display the logical and physical execution plans of a DataFrame in PySpark, which helps understand how Spark will execute the query. It does not compute statistical values like min, max, mean, or standard deviation. The community discussion unanimously supports this with 100% selecting 'No' (option B), and comments highlight that df.describe() or df.summary() should be used instead for statistical calculations. These alternatives provide the required summary statistics for string and numeric columns, making df.explain() unsuitable for the goal.
Author: LeetQuiz Editorial Team
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You have a Fabric tenant containing a new semantic model in OneLake. You are using 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.
You implement the following PySpark code:
df.explain()
df.explain()
Does this solution meet the goal?
A
Yes
B
No