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

Question

Options

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

• A
  3% (1)
• B
  6% (2)
• D
  3% (1)
• E
  87% (27)

Explanation

Option E is correct because PySpark DataFrames use the join method with the signature join(other, on, how) - making the correct call storesDF.join(employeesDF, "storeId", "outer"), where the second argument is the other DataFrame, the third is the join key as a string, and the fourth specifies the join type.

Options B and D are wrong because merge is a pandas method, not a PySpark DataFrame method - using it on a Spark DataFrame will raise an AttributeError.

Options A and C also use join, but likely have the parameters in the wrong order (e.g., swapping "storeId" and "outer", or passing the column name unquoted) - PySpark requires the join key before the join type, and string literals must be quoted.

Memory tip: Think "Spark JOINs, Pandas MERGEs." In PySpark, join follows SQL-like argument order: who, what column, what type - just like writing JOIN employeesDF ON storeId OUTER in SQL.

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