Correct Answer: C. Increased computation time due to internal frame conversion

Explanation: One of the potential downsides of using the pandas API on Spark (formerly Koalas) is the increased computation time that can occur due to internal conversion between Spark DataFrame and pandas DataFrame formats. When operations are performed using the pandas API on Spark, the data may need to be converted back and forth between the Spark DataFrame format and the pandas DataFrame format. This conversion process can introduce overhead, particularly for large datasets or complex operations.

Other Options:

• A: The pandas API on Spark aims to provide a pandas-like experience while offering much of the functionality of PySpark. While there might be some specific functionalities that differ, it is not generally characterized by limited functionality compared to PySpark.
• B: The data structure used by pandas API on Spark is not inherently inefficient. It is designed to work efficiently with Apache Spark‘s distributed data structures.
• D: The pandas API on Spark is designed specifically for distributed computing and leverages Apache Spark‘s capabilities. Therefore, limited support for distributed computing is not a concern.

In summary, while the pandas API on Spark offers the convenience of pandas syntax with the scalability of Spark, one should be mindful of the potential performance implications due to the internal data conversion processes, especially when dealing with large-scale data.