If tasks A and B complete successfully but task C fails during a scheduled run, what describes the resulting state?

The correct answer is that if tasks A and B complete successfully, but task C fails, all logic expressed in the notebooks associated with tasks A and B will have been successfully completed, and it is possible that some operations in task C may have completed successfully prior to the failure.

In a compute environment like Databricks, tasks are often run in parallel or in a sequence based on dependencies. When tasks A and B finish their execution successfully, it indicates that the operations contained within those tasks were executed without errors, and any resulting state changes they intended to perform were committed successfully.

However, with task C failing, the operations that were able to execute before the failure may have completed successfully, but any subsequent operations within task C that did not finish or were dependent on earlier parts of task C would not have been executed. This represents a scenario where partial completion of task C exists—some operations may have succeeded before the failure occurred.

The other options suggest a more absolute failure state or imply that tasks A and B would not have been able to complete due to dependency issues. However, in many orchestrated workflows, as long as there are no dependencies leading to a block of tasks A and B based on C's failure, A and B can successfully commit their