Which of the following code blocks returns a DataFrame with column storeSlogan where single quotes in column storeSlogan in DataFrame storesDF have been replaced with double quotes? A sample of…

Question

Exhibit

Options

• AstoresDF.withColumn("storeSlogan", col("storeSlogan").regexp_replace("'" """))
• BstoresDF.withColumn("storeSlogan", regexp_replace(col("storeSlogan"), "'"))
• CstoresDF.withColumn("storeSlogan", regexp_replace(col("storeSlogan"), "'", """))
• DstoresDF.withColumn("storeSlogan", regexp_replace("storeSlogan", "'", """))
• EstoresDF.withColumn("storeSlogan", regexp_extract(col("storeSlogan"), "'", """))

• A
  4% (2)
• B
  9% (4)
• C
  72% (33)
• D
  13% (6)
• E
  2% (1)

Explanation

regexp_replace(column, pattern, replacement) requires exactly three arguments: the column expression, the regex pattern to find, and the replacement string. Option C - storesDF.withColumn('storeSlogan', regexp_replace(col('storeSlogan'), "'", '"')) - is correct because it passes a proper col() reference and both the search and replacement strings. Option A has broken syntax (missing comma between pattern and replacement). Option B is missing the third replacement argument entirely. Option D passes the column name as a plain string 'storeSlogan' rather than using col('storeSlogan') - regexp_replace requires a Column object as its first argument. Option E uses regexp_extract, which extracts a matched substring rather than replacing it.

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