The correct answer is C. compute.default_index_type.

Explanation: Pandas API on Spark offers two main index types:

• Sequence: A simple integer index, which is the default but may not be the most efficient for large datasets.
• Distributed: Leverages Spark's distributed processing for better performance with large-scale operations.

The compute.default_index_type option allows you to set the default index type for new DataFrames. Choosing "distributed" can enhance performance for index-related operations in large datasets.

Why not the others?

• A. compute.default_index_cache: Manages index caching, not the index type.
• B. compute.ops_on_diff_frames: Governs operations on DataFrames with differing indexes, unrelated to default index type.
• D. compute.shortcut_limit: Limits the number of rows collected to the driver for certain operations, not related to index type.

Example Usage:

import pandas as pd
# Set default index type to "distributed"
pd.set_option("compute.default_index_type", "distributed")
# Create a DataFrame with a distributed index
df = pd.DataFrame({"A": [1, 2, 3, 4, 5], "B": [5, 4, 3, 2, 1]})
# More efficient index operations
df.sort_index()
df.loc[1:4]

Key Takeaways:

• Opt for a distributed index with large datasets or frequent index operations.
• Index type choice can significantly affect performance.
• Experiment to find the optimal setup for your needs.