A Databricks job has been configured with three tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
What will be the resulting state if tasks A and B complete successfully but task C fails during a scheduled run?
When a Databricks job is set up with multiple tasks where some tasks are dependent on others, the execution and success of tasks are crucial for the final outcome. Since task A does not depend on any other tasks, it will run independently and its completion will stand regardless of subsequent tasks' outcomes. Tasks B and C, however, depend on the successful completion of task A. Since task B is completed successfully, its logic will be executed and changes will be committed. Task C fails during execution; therefore, any partial or completed operations specific to it will be rolled back. However, since task C failing doesn't affect the commitment of changes from tasks A and B, all logic from A and B will be successfully executed and any changes from task C will be rolled back.
A: A. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully. Because this type of orchestration indicates a Fan-Out.