A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?
A.
In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
B.
In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
C.
In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
D.
In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark's cluster mode:
"The driver program runs on the cluster’s worker node instead of the client’s local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs."
This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.
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