The code block shown below should return a new DataFrame that is the result of an inner join between DataFrame storeDF and DataFrame employeesDF on column storeId. Choose the response chat correctly…

Question

Options

• A
  1. join
• B
  1. join
• C
  1. merge
• D
  1. join
• E
  1. join

• A
  2% (1)
• B
  6% (3)
• C
  4% (2)
• D
  87% (41)

Explanation

Option D correctly completes the PySpark DataFrame join with 1. join, 2. employeesDF, 3. "storeId", 4. "inner" - matching the PySpark API signature df.join(other, on, how) where you pass the other DataFrame first, then the join key, then the join type.

Why distractors are wrong:

• C uses merge - that's the pandas API, not PySpark's DataFrame API. PySpark DataFrames use .join(), not .merge().
• A, B, E also use join but get the parameter order or values wrong - common traps include swapping the column name and join type, using "storeId" as blank 2 (before the DataFrame), or omitting/misspelling "inner" as the join strategy.

Memory tip: Think of PySpark's .join() arguments as answering three questions in order - "Join with WHO? On WHAT column? In WHAT way?" → (employeesDF, "storeId", "inner"). If you remember pandas uses merge and PySpark uses join, you can immediately eliminate any option that crosses the two APIs.

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