Apache Spark Certification 2025 – 400 Free Practice Questions to Pass the Exam

In Apache Spark, accumulators are variables that are used to perform aggregations or accumulate results across the nodes in a cluster. When computations using accumulators are performed, the results are sent back to the driver program. This allows the driver to keep track of the aggregated values during the execution of tasks across the various executors.

The driver is essentially the coordinator of the Spark application. It executes the main program and is responsible for converting the user's code into tasks that are distributed across the worker nodes (executors). Since accumulators are primarily designed to provide feedback or aggregate statistics to the driver, the computed results are updated in the driver's memory. This makes it possible for the driver to access the final accumulated result after task execution.

The other options do not align with how accumulators function in Spark. For instance, while executors are responsible for executing the tasks, they do not retain the results of accumulator computations for themselves but instead report those results back to the driver. Similarly, accumulators do not directly send results to other cluster nodes or any storage system; their primary purpose is to facilitate collection of aggregate information back to the driver for further processing or analysis.