The Spark execution hierarchy from coarsest to finest is: Job → Stage → Task. A Job is triggered by an action (e.g., count(), save()) and represents the highest-level unit of work. A Job is divided into Stages, separated by shuffle boundaries (e.g., due to wide transformations like groupBy()). Each Stage consists of Tasks, which are the smallest units of work and operate on data partitions. Executor and Slot relate to resource allocation, not the logical execution hierarchy. Thus, the coarsest level is Job (B).