Which of the following code blocks returns a new DataFrame with column storeReview where the pattern "End" has been removed from the end of column storeReview in DataFrame storesDF? A sample…

Question

Options

• AstoresDF.withColumn("storeReview", col("storeReview").regexp_replace(" End$", ""))
• BstoresDF.withColumn("storeReview", regexp_replace(col("storeReview"), " End$", ""))
• CstoresDF.withColumn("storeReview? regexp_replace(col("storeReview"), " End$"))
• DstoresDF.withColumn("storeReview", regexp_replace("storeReview", " End$", ""))
• EstoresDF.withColumn("storeReview", regexp_extract(col("storeReview"), " End$", ""))

• B
  73% (16)
• C
  5% (1)
• D
  14% (3)
• E
  9% (2)

Explanation

regexp_replace is a standalone function from pyspark.sql.functions - it is NOT a method chained off a Column object (eliminating A). It requires exactly three arguments: the column expression, the regex pattern, and the replacement string (eliminating C which omits the replacement). Passing a plain string 'storeReview' instead of col('storeReview') as the first argument is incorrect (eliminating D). regexp_extract is for capturing matches, not removing them (eliminating E). Option B is correct: regexp_replace(col('storeReview'), ' End$', '') uses the '$' anchor to match only at the end of the string and replaces it with an empty string.

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