Explanation:
Let's analyze each option:
distinct() method does not accept any arguments and removes duplicates based on all columns in the DataFrame.distinct() and dropDuplicates() are supported in Databricks, and neither is deprecated.drop_duplicates() is an alias for dropDuplicates(), making them interchangeable.dropDuplicates() accepts column names as arguments for targeted de-duplication.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.
Ultimate access to all questions.
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.
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