Apache Spark Certification Practice Test

In Spark, the term "worker" refers to a node that is responsible for executing tasks. Workers are an essential part of Spark's architecture, as they are where the actual computation happens. Each worker node runs one or more executor processes that are assigned tasks from the driver program, which coordinates the overall execution.

The worker nodes pull data from memory or disk, execute the tasks assigned to them, and then return results to the driver. This parallel execution allows Spark to process large datasets efficiently across a cluster of machines, leveraging distributed computing.

Understanding the role of a worker is crucial for grasping how Spark manages resources and executes jobs effectively. This architecture enables Spark to optimize performance by distributing workloads across multiple nodes, making it a powerful tool for large-scale data processing.

The other options relate to different aspects of Spark's ecosystem. A dedicated server for storage refers to storage components, such as HDFS or S3, where data is managed but does not perform computation. A computational unit in Spark could refer to various concepts but does not specifically denote the role of a worker. A client application to submit jobs typically refers to a user interface or command-line tool through which jobs are submitted to the Spark cluster, which is distinct from the execution role fulfilled by worker