Apache Spark Certification Practice Test

The two types of "shared" variables in Spark are broadcast variables and accumulators.

Broadcast variables allow the programmer to efficiently send data to all worker nodes. When there’s a large dataset that needs to be used across different tasks, broadcasting it minimizes data transfer overhead, as the same value is sent to all nodes instead of sending a copy of the data with each task. This is especially useful when working with large read-only datasets, improving performance and resource utilization.

Accumulators are a different kind of shared variable used for aggregating values across multiple tasks. They provide a way to implement counters or sums that can be updated during task execution. Accumulators can be used to collect metrics or debug information from different nodes in a parallel computation. Unlike broadcast variables, accumulators allow tasks to increment their values, but they only provide a final result after all tasks have completed, ensuring consistency across distributed processes.

The other options do not represent types of shared variables in Spark. Variables and arrays, lists and maps, or reference and value types are general programming concepts but do not specifically pertain to the unique shared variable types in Apache Spark.