The code block shown below contains an error. The code block is intended to return a new DataFrame that is the result of a position-wise union between DataFrame storesDF and DataFrame…

Question

Options

• Aconcat(storesDF, acquiredStoresDF)
• BstoresDF.unionByName(acquiredStoresDF)
• Cunion(storesDF, acquiredStoresDF)
• DunionAll(storesDF, acquiredStoresDF)
• EstoresDF.union(acquiredStoresDF)

• B
  2% (1)
• C
  2% (1)
• D
  4% (2)
• E
  91% (41)

Explanation

storesDF.union(acquiredStoresDF) is the correct PySpark syntax because union() is an instance method on a DataFrame object that performs a position-wise row combination - equivalent to SQL's UNION ALL.

Why the distractors fail:

• A & C - concat() and union() as standalone functions don't exist in PySpark's DataFrame API; concat is a string/array column function, not a DataFrame-level one.
• B - unionByName() is a real PySpark method, but it matches columns by name, not by position - the wrong behavior for this task.
• D - unionAll() was the original method but was deprecated in Spark 2.0 and replaced by union(); calling it as a standalone function is doubly wrong.

Memory tip: In PySpark, DataFrame operations are almost always method calls on an existing DataFrame (df.someOperation()), not standalone functions. If you see a union operation written without a DataFrame receiver (like options A, C, D), it's a red flag.

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