Correct Answer: E

pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata.

Explanation: pandas API on Spark DataFrames are built on top of native Spark DataFrames. They combine the functionality of Spark DataFrames with additional metadata and capabilities that allow them to emulate pandas DataFrames. This means that while they operate similarly to pandas DataFrames, they are backed by the distributed computing power of Spark. The additional metadata helps in maintaining compatibility with pandas-like functionality.

Other Options:

• A: While pandas API on Spark DataFrames provide a pandas-like interface, they are not single-node versions of Spark DataFrames. They still leverage the distributed computing capabilities of Spark.
• B: pandas API on Spark DataFrames are closely related to Spark DataFrames as they are built upon them and extend their capabilities.
• C: The mutability of pandas API on Spark DataFrames is not a defining distinction from Spark DataFrames.
• D: The performance comparison is not straightforward. pandas API on Spark DataFrames are designed to bring pandas-like functionality to Spark DataFrames and leverage Spark's distributed nature, but this doesn‘t inherently make them more performant than native Spark DataFrames in all scenarios.

In summary, pandas API on Spark DataFrames extend the capabilities of native Spark DataFrames by adding metadata and functionality to provide a pandas-like experience while still benefiting from Spark's distributed computing features.