Answer: (storesDF.withColumn("storeValueCategory", split(col("storeCategory"), "_")[0]) .withColumn("storeSizeCategory", split(col("storeCategory"), "_")[1])), (storesDF.withColumn("storeValueCategory", split("storeCategory", "_")[0]) .withColumn("storeSizeCategory", split("storeCategory", "_")[1]))

The question requires splitting the `storeCategory` column into two new columns using the underscore `_` as the delimiter. The correct approach uses the `split` function from PySpark's `functions` module, which returns an array. The first element (index 0) of the array corresponds to the part before the underscore, and the second element (index 1) corresponds to the part after. Option C correctly uses `split(col('storeCategory'), '_')[0]` and `[1]` to extract the two parts. The `split` function is applied to the column, and array indices are used to access the split elements. Option D is also correct because `split('storeCategory', '_')` is equivalent to `split(col('storeCategory'), '_')`. In PySpark, passing a string to `split` treats it as a column name, so the syntax in D is valid and produces the same result as C. Options A and E incorrectly use indices 1 and 2, which would result in out-of-bounds errors or null values. Option B uses `col.split()`, which is not the correct method for splitting a column in PySpark.

Author: LeetQuiz Editorial Team