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

The choice that correctly identifies the Spark mode allowing for dynamic allocation of resources based on workload is YARN. YARN, which stands for Yet Another Resource Negotiator, is an integral part of the Hadoop ecosystem and is designed to efficiently manage resources across the cluster. It enables Spark to dynamically allocate executors based on the current workload, facilitating better resource utilization and improved performance.

In a YARN environment, Spark can request additional resources when there is a spike in workload or release them when they are no longer needed, allowing for a more flexible and responsive computing environment. This capability is particularly beneficial for applications with variable workloads, as it helps in achieving scalability without manual adjustments.

Other modes, such as Standalone and Mesos, have their own resource management features but lack the same level of dynamic resource allocation driven by workload patterns as provided by YARN. Local mode, on the other hand, runs Spark on a single machine and does not involve resource allocation across multiple nodes, making it unsuitable for dynamic resource management in a cluster environment. Therefore, YARN is the preferred choice for dynamic allocation in Spark.