Apache Spark Certification Practice Test 2025 – All-in-One Guide to Excel in Your Certification Exam

The driver program in Apache Spark is responsible for handling task scheduling. It acts as the central coordinator of the Spark application, maintaining information about the Spark application's transformations and actions. As it processes the application code and builds a logical execution plan, the driver program schedules tasks across the available executors and worker nodes.

The driver program divides the work into smaller tasks that are executed in parallel, managing the execution of these tasks while ensuring that resources are allocated efficiently. Moreover, it communicates with the cluster manager to negotiate resources for the tasks it wants to run on the available worker nodes, effectively orchestrating the entire computation process.

In contrast, other components like the cluster manager manage resources across the cluster but do not directly handle the scheduling of tasks, while executors are responsible for executing the tasks assigned to them by the driver, and worker nodes host these executors, providing the computational resources needed for task execution. Thus, the driver program's role in task scheduling makes it the key component in this context.