Which of the following operations performs a cross join on two DataFrames?

Question

Options

• ADataFrame.join()
• BThe standalone join() function
• CThe standalone crossJoin() function
• DDataFrame.crossJoin()
• EDataFrame.merge()

• A
  9% (2)
• C
  4% (1)
• D
  87% (20)

Explanation

DataFrame.crossJoin() is the correct method for performing a cross join (Cartesian product) in PySpark - it's an instance method called directly on a DataFrame, e.g., df1.crossJoin(df2), and returns every combination of rows between the two DataFrames.

Why the distractors are wrong:

• A. DataFrame.join() - performs conditional joins (inner, left, right, outer) using a specified condition or key column, not a cross join by default.
• B. Standalone join() function - no such standalone function exists in PySpark's DataFrame API for this purpose.
• C. Standalone crossJoin() function - crossJoin is not a standalone function; it only exists as a method on a DataFrame instance.
• E. DataFrame.merge() - this is a pandas method; PySpark DataFrames do not have a merge() method.

Memory tip: Think "cross is personal" - the cross join belongs to the DataFrame object itself (df.crossJoin()), not to any standalone function. If you remember that PySpark uses instance methods for joins, you can eliminate B and C immediately.

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