Answer-first summary for fast verification
Answer: The methods `dropDuplicates()` and `drop_duplicates()` are interchangeable, as per the official Spark documentation.
Let's analyze each option: - **A**: Incorrect. The `distinct()` method does not accept any arguments and removes duplicates based on all columns in the DataFrame. - **B**: Incorrect. Both `distinct()` and `dropDuplicates()` are supported in Databricks, and neither is deprecated. - **C**: Correct. `drop_duplicates()` is an alias for `dropDuplicates()`, making them interchangeable. - **D**: Incorrect. Only `dropDuplicates()` accepts column names as arguments for targeted de-duplication. - **E**: Incorrect. `distinct()` can be used on both RDDs and DataFrames, whereas `dropDuplicates()` is only for DataFrames. For further details, refer to the Spark documentation on `dropDuplicates()`, `drop_duplicates()`, and the `distinct()` method for both RDDs and DataFrames.
Author: LeetQuiz Editorial Team
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A data engineer is examining the distinct() and dropDuplicates() methods in Spark for de-duplicating a DataFrame. Which statement accurately describes the use of these methods for de-duplication?
A
The distinct() method can be used to remove duplicates based on specific columns by passing column names as arguments.
B
In Databricks, the distinct() method is deprecated, leaving dropDuplicates() as the only supported method for de-duplication.
C
The methods dropDuplicates() and drop_duplicates() are interchangeable, as per the official Spark documentation.
D
Both distinct() and dropDuplicates() methods allow for the removal of duplicates based on specific columns.
E
The dropDuplicates() method is restricted to RDDs, while the distinct() method is exclusively for DataFrames.