Which of the following code blocks returns a DataFrame containing only the rows from DataFrame storesDF where the value in column sqft is less than or equal to 25,000 OR the value in column…

Question

Options

• AstoresDF.filter(col("sqft") <= 25000 | col("customerSatisfaction") >= 30)
• BstoresDF.filter(col("sqft") <= 25000 or col("customerSatisfaction") >= 30)
• CstoresDF.filter(sqft <= 25000 or customerSatisfaction >= 30)
• DstoresDF.filter(col(sqft) <= 25000 | col(customerSatisfaction) >= 30)
• EstoresDF.filter((col("sqft") <= 25000) | (col("customerSatisfaction") >= 30))

• A
  7% (2)
• B
  4% (1)
• C
  18% (5)
• E
  71% (20)

Explanation

When combining Spark column conditions with OR, you must use the bitwise | operator (not Python's or keyword) and wrap each individual condition in parentheses. This is required because of Python operator precedence: without parentheses, | binds more tightly than comparison operators like <= and >=, causing incorrect evaluation. Option A is missing the parentheses around each condition, making it evaluate as col("sqft") <= (25000 | col(...)) >= 30, which is wrong. Options B and C use Python's or keyword, which doesn't work with Spark Column objects. Option D passes unquoted variable names to col(). Only E - (col("sqft") <= 25000) | (col("customerSatisfaction") >= 30) - is correct.

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