The correct answer is C. display.max_rows. This option is specifically designed to control the maximum number of rows shown when printing DataFrames or Series in the Pandas API on Spark. It's crucial for managing output readability and preventing the display of overwhelming amounts of data, especially with large datasets.

• Incorrect Options:
  • A. plotting.max_rows: This option limits the number of rows displayed in plots, not general output.
  • B. compute.default_index_cache: This option controls caching behavior for indexes, not output display.
  • D. compute.ops_on_diff_frames: This option manages operations on different DataFrames, not output display.

How to Use display.max_rows:

1. Import:

   from pyspark.pandas import config
   

   from pyspark.pandas import config
   
2. Set Maximum Rows:

   config.set_option("display.max_rows", 10)  # Set to 10 rows for example
   

   config.set_option("display.max_rows", 10)  # Set to 10 rows for example
   
3. Print Output:

   df = spark.createDataFrame([(1, "a"), (2, "b"), (3, "c"), (4, "d")], ["id", "value"])
   print(df)  # Will now display only 10 rows
   

   df = spark.createDataFrame([(1, "a"), (2, "b"), (3, "c"), (4, "d")], ["id", "value"])
   print(df)  # Will now display only 10 rows
   

Key Points:

• Use display.max_rows to control output verbosity in Pandas API on Spark.
• Adjust the value based on your dataset size and desired level of detail.
• Remember to import the config module for configuration access.