Databricks Certified Machine Learning - Associate
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In the context of using Pandas API on Spark, explain the key differences between Spark DataFrames and Pandas on Spark DataFrames, and how these differences might impact the performance of a data processing task.
A
Spark DataFrames are optimized for distributed computing, while Pandas on Spark DataFrames are not.
B
Pandas on Spark DataFrames can be used for distributed computing, but they are slower than Spark DataFrames due to the usage of an InternalFrame.
C
Spark DataFrames and Pandas on Spark DataFrames are identical in terms of performance and functionality.
D
Pandas on Spark DataFrames are faster than Spark DataFrames because they utilize the Pandas library for data manipulation.