Exam DP-600 All Questions

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Exam DP-600 topic 1 question 56 discussion

Actual exam question from Microsoft's DP-600

Question #: 56
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.summary()
Does this meet the goal?

A. Yes
B. No

Show Suggested Answer

Suggested Answer: A 🗳️

by Momoanwar at Feb. 17, 2024, 9:44 p.m.

Comments

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stilferx

Highly Voted 1 year, 1 month ago

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

upvoted 9 times

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Lotusss

Most Recent 2 months, 3 weeks ago

Selected Answer: B

df.summary() alone wont cut it. df.summary().show() is correct. So anwser is B

upvoted 1 times

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b01d700

4 months, 3 weeks ago

Selected Answer: B

The correct PySpark expression to calculate min, max, mean, and standard deviation for both numeric and STRING columns is: df.describe()

upvoted 1 times

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slu239

6 months, 1 week ago

Selected Answer: B

Not meet the goal because it has to be df.summary().show()

upvoted 1 times

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2fe10ed

6 months, 3 weeks ago

Selected Answer: A

https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.summary.html#pyspark.sql.DataFrame.summary

upvoted 1 times

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Pegooli

11 months, 1 week ago

Selected Answer: B

Using df.summary() in PySpark will provide summary statistics, including min, max, mean, and standard deviation for all numeric columns. However, it will not provide these statistics for string columns since summary statistics like min, max, mean, and standard deviation are not applicable to string data.

upvoted 2 times

gover07

10 months ago

so the questions doesn't make sense if you are asked to calculate things that aren't defined

upvoted 2 times

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6d1de25

11 months, 3 weeks ago

Selected Answer: A

Correct

upvoted 1 times

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7d97b62

12 months ago

Selected Answer: 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.

upvoted 3 times

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282b85d

1 year, 1 month ago

Selected Answer: 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.

upvoted 4 times

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XiltroX

1 year, 4 months ago

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

upvoted 1 times

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SamuComqi

1 year, 4 months ago

Selected Answer: 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

upvoted 4 times

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Momoanwar

1 year, 4 months ago

Selected Answer: A

Correct

upvoted 2 times

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