Which of the following code blocks fails to return the number of rows in DataFrame storesDF for each distinct combination of values in column division and column storeCategory?

Question

Options

• AstoresDF.groupBy((col("division"), col("storeCategory")]).count()
• BstoresDF.groupBy("division").groupBy("storeCategory").count()
• CstoresDF.groupBy(["division", "storeCategory"]).count()
• DstoresDF.groupBy("division", "storeCategory").count()
• EstoresDF.groupBy(col("division?, col("storeCategory")).count()

• A
  3% (1)
• B
  88% (30)
• C
  6% (2)
• D
  3% (1)

Explanation

Chaining two separate groupBy() calls does not combine both columns into a single grouping. The second groupBy() call completely replaces the first, so the result groups only by storeCategory - division is silently ignored. The correct approaches all pass both columns in a single groupBy() call: as multiple string arguments (.groupBy('division', 'storeCategory')), as a list (.groupBy(['division', 'storeCategory'])), or as Column objects (.groupBy(col('division'), col('storeCategory'))). Options A, C, D, and E all achieve the intended two-column grouping.

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