Answer-first summary for fast verification
Answer: storesDF.groupBy(“division”, “storeCategory”).count()
The correct answer is determined by understanding how the `groupBy` method works in Spark. To count rows for each combination of columns, `groupBy` must receive the column names as separate strings. Option C correctly passes the column names as individual string arguments. Option A uses a `Seq` of `Column` objects, which is invalid syntax. Option B references undefined variables. Option D chains `groupBy` incorrectly, and Option E uses a `Seq` of strings, which isn't the correct parameter type.
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Which of the following code blocks correctly counts the number of rows in DataFrame storesDF for each unique combination of values in the columns division and storeCategory?
A
storesDF.groupBy(Seq(col(“division”), col(“storeCategory”))).count()
B
storesDF.groupBy(division, storeCategory).count()
C
storesDF.groupBy(“division”, “storeCategory”).count()
D
storesDF.groupBy(“division”).groupBy(“StoreCategory”).count()
E
storesDF.groupBy(Seq(“division”, “storeCategory”)).count()