Which of the following operations fails to return a DataFrame with no duplicate rows?

The correct answer is E. DataFrame.drop_duplicates(subset = "all"). In PySpark, dropDuplicates() and its alias drop_duplicates() accept an optional subset parameter that takes either None (meaning all columns) or a list of column names. Passing subset=None explicitly is valid and means 'consider all columns,' which is identical to calling the…

DataFrame Transformations

Question

Options

ADataFrame.dropDuplicates()
BDataFrame.distinct()
CDataFrame.drop_duplicates()
DDataFrame.drop_duplicates(subset = None)
EDataFrame.drop_duplicates(subset = "all")

How the community answered

(29 responses)

A
3% (1)
C
3% (1)
E
93% (27)

Explanation

In PySpark, dropDuplicates() and its alias drop_duplicates() accept an optional subset parameter that takes either None (meaning all columns) or a list of column names. Passing subset=None explicitly is valid and means 'consider all columns,' which is identical to calling the method with no arguments. However, subset="all" is not a valid argument - the string "all" is not recognized as a special keyword and is not a column name, so this call raises an AnalysisException or behaves unexpectedly. Options A, B, C, and D all correctly remove duplicate rows; only E fails.

Topics

#PySpark DataFrame API#Data Deduplication#DataFrame Transformations#Method Parameters

Community Discussion

No community discussion yet for this question.

Full DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Practice