Answer: When another task needs to successfully complete before the new task begins

## Explanation The correct answer is **E** because the 'Depends On' field in Databricks Jobs is specifically designed to define task dependencies where one task must successfully complete before another task can begin execution. ### Key Points: 1. **Task Dependencies in Databricks**: The 'Depends On' field establishes execution order dependencies between tasks in a Databricks Job. 2. **Success Requirement**: When Task B depends on Task A, Task B will only start if Task A completes successfully. If Task A fails, Task B will not run. 3. **Workflow Orchestration**: This feature enables creating complex workflow pipelines where certain tasks must complete before others can proceed. ### Why Other Options Are Incorrect: - **A**: Tasks are not 'replaced' by other tasks; they execute independently based on dependencies. - **B**: The 'Depends On' field requires successful completion, not failure. For failure-based dependencies, Databricks provides different mechanisms. - **C**: Dependency libraries are configured at the task level, not through the 'Depends On' field. - **D**: Compute resource management is handled through cluster configuration and task settings, not through task dependencies. ### Practical Use Case: In a typical ETL pipeline, you might have: 1. Task A: Extract data from source 2. Task B: Transform data (depends on Task A) 3. Task C: Load data to target (depends on Task B) This ensures data flows through the pipeline in the correct order without data integrity issues.

Author: Keng Suppaseth