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

The correct answer is Executors. In Apache Spark, Executors are the components that run on worker nodes and are responsible for executing tasks and storing data for the application. When a Spark application is launched, the driver program is responsible for converting the user program into tasks that can be distributed across the cluster. These tasks are then sent to Executors, which run them in parallel.

Executors are vital because they handle the actual computation and manage storage for both the input data and the intermediate results of computations. They also provide fault tolerance; if an Executor fails, the driver can reassign tasks to another available Executor. This design allows Spark to efficiently process large datasets across multiple nodes, leveraging the distributed computing framework.

The other components have specific roles: the Driver orchestrates the execution but does not directly perform tasks on the data itself; Masters manage the cluster resources but do not execute tasks; and threads are a lower-level programming concept related to execution within a single process, not a component specific to Spark's distributed architecture. Understanding the role of Executors is crucial for grasping how Spark achieves scalability and efficiency in big data processing.