Databricks Certified Data Engineer - Associate

Explanation

In Databricks Jobs, the Depends On field is used to define task dependencies. When you select a task in the Depends On field of a new task, you are specifying that the new task should only start after the selected task has successfully completed.

Key Points:

1. Task Dependencies: The Depends On field establishes a dependency relationship between tasks in a Databricks Job.
2. Success Requirement: The dependent task (the one selected in Depends On) must complete successfully for the new task to begin execution.
3. Workflow Orchestration: This is fundamental to creating sequential workflows where certain tasks must complete before others can start.

Why Other Options Are Incorrect:

• A: Tasks are not "replaced" by dependencies - dependencies define execution order, not replacement.
• B: Dependencies require successful completion, not failure.
• C: Dependency libraries are configured separately in task settings, not through the Depends On field.
• D: Resource usage is managed through cluster configuration and task settings, not through task dependencies.

Practical Example:

In an 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 destination (depends on Task B)

Task B would have Task A in its Depends On field, ensuring transformation only starts after successful extraction.