Exam DP-600 topic 1 question 54 discussion

Actual exam question from Microsoft's DP-600

Question #: 54
Topic #: 1

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.explain()
Does this meet the goal?

A. Yes
B. No

Show Suggested Answer

Suggested Answer: B 🗳️

by Momoanwar at Feb. 17, 2024, 9:43 p.m.

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282b85d

Highly Voted 6 months, 3 weeks ago

Selected Answer: B

The df.explain() method in PySpark is used to print the logical and physical plans of a DataFrame, which helps in understanding how Spark plans to execute the query. It does not compute any statistical values like min, max, mean, or standard deviation. **To achieve the goal, you should use: df.describe().show()

upvoted 9 times

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SamuComqi

Highly Voted 10 months ago

Selected Answer: B

The correct syntax is df.describe(). Sources: * describe --> https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.describe.html * explain --> https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.explain.html

upvoted 5 times

SamuComqi

10 months ago

Also df.summary() is a valid solution. Source ---> https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.summary.html

upvoted 1 times

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