The code block shown below should return a DataFrame containing all columns from DataFrame storesDF except for column sqft and column customerSatisfaction. Choose the response that correctly fills…

Question

Options

• A
  1. drop
• B
  1. storesDF
• C
  1. storesDF
• D
  1. storesDF
• E
  1. drop

• A
  3% (1)
• C
  83% (25)
• D
  3% (1)
• E
  10% (3)

Explanation

Option C correctly places storesDF in position 1 because the dot-notation chain must begin with the DataFrame object - you call methods on the DataFrame, not the other way around. This gives the pattern storesDF.drop("sqft", "customerSatisfaction"), which is the standard PySpark syntax for removing named columns while returning all others.

Options A and E are wrong because they place drop in position 1, making it the calling object - drop is a method, not a DataFrame, so drop.something(...) is syntactically meaningless. Options B and D also start with storesDF but differ in positions 2 and/or 3 - likely using an incorrect method name (e.g., remove, delete) or wrong argument format, neither of which exists on a PySpark DataFrame.

Memory tip: Think of it as a sentence - "the DataFrame drops the columns" - so the DataFrame (storesDF) always comes first, .drop() is the verb, and the column names are the objects being acted on.

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