A Spark job will fail if the driver node fails (E) because the driver is responsible for coordinating the job's tasks and maintaining the application's state. Without the driver, the job cannot proceed. Options A, B, and C describe scenarios that do not necessarily cause a job to fail: not pulling data to the driver (A) is acceptable if the job doesn't require it, caching data larger than an executor's memory (B) may lead to performance issues but not failure as Spark can spill to disk, and data spilling from memory to disk (C) is a normal part of Spark's operation to handle large datasets. A failed worker node (D) may cause tasks to be retried but does not immediately cause the job to fail as Spark can recover by rerunning tasks on other nodes.