The code block shown below contains an error. The code block intended to return a new DataFrame that is the result of an inner join between DataFrame storesDF and DataFrame employeesDF on column…

Question

Options

• AThe key column storeId needs to be a string like "storeId".
• BThe key column storeId needs to be specified in an expression of both Data Frame columns like
• CThe default argument to the joinType parameter is "inner" - an additional argument of "left" must
• DThere is no DataFrame.join() operation - DataFrame.merge() should be used instead.
• EThe key column storeId needs to be wrapped in the col() operation.

• A
  4% (1)
• B
  8% (2)
• C
  88% (23)

Explanation

Option C is correct because StoresDF uses a capital "S" while the actual DataFrame variable is named storesDF - both Scala and Python are case-sensitive, so this would cause a NameError at runtime. The .join() call itself is syntactically valid; Spark's default joinType is already "inner", meaning no third argument is required to get an inner join.

Why the distractors are wrong:

• A is wrong because "storeId" inside Seq("storeId") is already a string - no change needed there.
• B is wrong because the Seq("storeId") shorthand (joining on a shared column name) is a perfectly valid alternative to writing a full column expression like storesDF("storeId") === employeesDF("storeId").
• D is wrong because DataFrame.join() absolutely exists in Spark; .merge() is a pandas method and does not apply here.
• E is wrong because col() wrapping is only needed when you pass a column expression, not when using the Seq / string shorthand for join keys.

Memory tip: Whenever a code snippet looks almost right but throws an unexpected error, check variable name casing first - Spark (Scala/Python) is case-sensitive, and a mismatched StoresDF vs storesDF is a classic exam trap. Remember: default join = inner, so no third argument needed.

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