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Answer: Define each task as a separate job and use dependencies to manage the execution order
The correct approach is to define each task as a separate job and use dependencies to manage the execution order. This method allows for granular control over each task, ensuring that they are executed in the correct sequence and that any dependencies are properly managed, which is crucial for the successful orchestration of multi-task ML workflows.
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In a multi-task ML workflow scenario, you need to orchestrate a series of tasks using Databricks jobs. Describe how you would set up these jobs to ensure that they are executed in the correct order and that dependencies between tasks are properly managed.
A
Manually trigger each task in the correct order
B
Use a single Databricks job to sequentially execute all tasks
C
Define each task as a separate job and use dependencies to manage the execution order
D
Use a combination of Databricks jobs and external scheduling tools to manage task dependencies