How are overlapping columns handled when joining DataFrames in Snowpark?

Question

Options

• ASnowpark will return a join result containing multiple columns that have the same names.
• BSnowpark will return the error: 'Ambiguous column'.
• CSnowpark will prepend a randomly-generated prefix to the overlapping columns in the join result.
• DSnowpark will append the DataFrame names to new columns in the join result.

• A
  6% (2)
• B
  89% (32)
• C
  3% (1)
• D
  3% (1)

Explanation

When two DataFrames share a column name and you join them in Snowpark without aliasing, Snowpark raises an 'Ambiguous column' error because it cannot determine which DataFrame's column you're referring to in subsequent operations - option B is correct. Option A is wrong because Snowpark explicitly rejects the ambiguity rather than silently returning duplicate column names (that behavior is more characteristic of pandas). Option C is wrong because Snowpark does not auto-generate prefixes; that kind of automatic renaming is seen in frameworks like PySpark's select with aliases, not Snowpark's default join behavior. Option D is wrong because Snowpark has no mechanism to append DataFrame variable names to column labels - you must resolve ambiguity yourself by aliasing columns before the join or using DataFrame.rename().

Memory tip: Think of Snowpark as strict and SQL-like - just as SQL throws an "ambiguous column" error when you reference an unqualified column that exists in multiple joined tables, Snowpark mirrors that strictness. The fix is always explicit: alias or rename before joining.

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