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Question 56

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.summary()

Does this meet the goal?

    Correct Answer: A

    Using the PySpark expression df.summary() will provide the min, max, mean, and standard deviation values for both numeric and string columns. The df.summary() function in PySpark is specifically designed to compute these statistics for all types of columns within the DataFrame, thereby meeting the goal of evaluating the data thoroughly.

Discussion
stilferxOption: A

IMHO, A Example: df1 = spark.createDataFrame([(1, 10), (2, 10), (2, 15)], schema = ['fruit_id', 'amount']) df1.summary() summary fruit_id amount count 3 3 mean 1.6666666666666667 11.666666666666666 stddev 0.5773502691896257 2.886751345948129 min 1 10 25% 1 10 50% 2 10 75% 2 15 max 2 15

SamuComqiOption: A

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

282b85dOption: B

while df.summary() does provide valuable information for numeric columns, it does not fully meet the goal of evaluating both string and numeric columns with the required statistical measures. Use df.summary() and df.agg() to cover numeric columns, and additional custom aggregations for string columns.

7d97b62Option: A

In pandas, use df.describe() for summary statistics of numeric columns. In PySpark, use df.summary() for summary statistics of both numeric and string columns in a distributed computing environment.

MomoanwarOption: A

Correct

6d1de25Option: A

Correct

XiltroXOption: A

df.summary() is the only option where you can get MIX, MAX and AVG