In the context of Pandas UDFs, explain the concept of caching and its benefits when working with intermediate datasets in Spark. Provide an example of how you would use caching in a Pandas UDF.

Simulated

Caching is a technique where intermediate datasets are stored in memory to enable faster access and processing.

86.4%

Caching is a technique where intermediate datasets are stored on disk to save storage space.

0.0%

Caching is a technique where intermediate datasets are partitioned across multiple nodes in a cluster to enable parallel processing.

13.6%

Caching is not applicable when working with Pandas UDFs in Spark.

0.0%

Databricks Certified Machine Learning - Associate