Which of the following code blocks returns a DataFrame where column storeCategory from DataFrame storesDF is split at the underscore character into column storeValueCategory and column storeSizeCatego

Question

Exhibit

Options

• A(storesDF.withColumn("storeValueCategory", split(col("storeCategory"), "_")[1])
• B(storesDF.withColumn("storeValueCategory", col("storeCategory").split("_")[0])
• C(storesDF.withColumn("storeValueCategory", split(col("storeCategory"), "_")[0])
• D(storesDF.withColumn("storeValueCategory", split("storeCategory", "_")[0])
• E(storesDF.withColumn("storeValueCategory", col("storeCategory").split("_")[1])

• A
  5% (3)
• C
  81% (52)
• D
  11% (7)
• E
  3% (2)

Explanation

The split() function in PySpark (from pyspark.sql.functions) takes a Column object and a delimiter string, and returns an array column. Array elements are accessed with 0-based integer indexing using square brackets. To get the first element (before the underscore), use index [0]. Option C - split(col("storeCategory"), "_")[0] - is correct for extracting the first part. Option A uses index [1] (the second element), which would give storeSizeCategory, not storeValueCategory. Options B and E incorrectly call .split() as a method on a Column object, which is not a valid PySpark API. Option D passes a plain string to split() instead of a Column object.

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