Databricks Certified Data Engineer - Associate

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.