Which scenario best describes the use of Spark broadcast?

The use of Spark broadcast is best described by scenarios in which there is a need to share immutable data efficiently across multiple tasks running on different nodes. Broadcasting allows the driver program to send a read-only variable, commonly referred to as a broadcast variable, to all nodes in the Spark cluster. This is particularly beneficial because it minimizes the amount of data that needs to be serialized and sent over the network multiple times.

When tasks require access to the same data, broadcasting is preferred to avoid the overhead of multiple data transfers. Instead of copying the data for every task, broadcasting sends it out once, allowing all nodes to access it concurrently.

This method is advantageous when the dataset is relatively small and needs to be referenced frequently across different tasks, such as lookup tables or static reference data that remains unchanged throughout the execution. Consequently, option C accurately captures the essence of using Spark broadcast for efficiently sharing immutable data across tasks.