Explanation:
✅ Correct Answer: D. Download the JSON equivalent of the job from the Job’s page.
Databricks allows the export of a Job's configuration as a JSON file, which includes details like the Job’s schedule, tasks, parameters, and cluster configuration. This JSON file can be stored in a version control system (e.g., Git) for tracking changes, comparing versions, and reverting configurations if necessary. This method provides a version-controllable representation of the Job’s setup.
❌ Incorrect Options:
A. Submit the Job once on an all-purpose cluster. Executing the Job does not offer version control for its configuration.
B. Download the XML description of the Job from the Job’s page. Databricks does not support exporting Job configurations in XML format.
C. Link the Job to notebooks that are part of a Databricks Repo. While this ensures version control for the notebooks, it does not cover the Job’s schedule and other configurations.
E. Submit the Job once on a Job cluster. Similar to option A, running the Job does not provide a mechanism for version controlling its configuration.
Ultimate access to all questions.
How can a data engineer achieve version control for a Job's schedule and configuration in Databricks?
A
Submit the Job once on an all-purpose cluster.
B
Download the XML description of the Job from the Job’s page.
C
Link the Job to notebooks that are part of a Databricks Repo.
D
Download the JSON equivalent of the job from the Job’s page.
E
Submit the Job once on a Job cluster.
No comments yet.