Option E is incorrect because the driver program in Spark is not reassigned to another worker node if the driver's node fails. The driver program is responsible for the coordination and control of the Spark application and runs on a separate machine, typically the client machine or cluster manager. If the driver's node fails, the Spark application as a whole may fail or need to be restarted, but the driver is not automatically reassigned to another worker node.

If the driver node fails your cluster will fail. If the worker node fails, Databricks will spawn a new worker node to replace the failed node and resumes the workload.

Option E beacuse spark doesn't assiggned driver if faild

The incorrect statement about Spark's stability is: E. Spark will reassign the driver to a worker node if the driver’s node fails. Explanation: Option A is correct because Spark is designed to handle the failure of worker nodes. When a worker node fails, Spark redistributes the lost tasks to other available worker nodes to ensure fault tolerance. Option C is correct because Spark is able to recompute data that was cached on failed worker nodes. Spark maintains lineage information about RDDs (Resilient Distributed Datasets), allowing it to reconstruct lost data partitions in case of failures.

The driver is responsible for maintaining spark context. If it fails, there is no recourse. The driver can mitigate the failure of worker nodes through limited fault tolerance mechanisms.

All of the following statements about Spark's stability are correct except for: E. Spark will reassign the driver to a worker node if the driver’s node fails. The driver is a special process in Spark that is responsible for coordinating tasks and executing the main program. If the driver fails, the entire Spark application fails and cannot be restarted. Therefore, Spark does not reassign the driver to a worker node if the driver's node fails.

The E is only valid when spark-submit is in cluster modes