Data Engineering Associate with Databricks Practice Exam

How can a data engineer achieve a version-controllable configuration of a Job's schedule?

1. By creating a local copy of the Job configuration file.

2. By downloading the JSON description of the Job from its page.

3. By using a third-party scheduling tool.

4. By directly editing the configuration in the Databricks interface.

The correct answer is: By downloading the JSON description of the Job from its page.

Achieving a version-controllable configuration of a Job's schedule is essential for maintaining and tracking changes over time. By downloading the JSON description of the Job from its page, a data engineer can capture the entire configuration of the Job, including its schedule, parameters, and settings. This JSON file can then be stored in a version control system, such as Git. This allows the team to keep track of configuration changes, review history, and roll back to previous configurations if necessary. Using JSON provides a structured format that can be easily modified, tracked, and compared with previous versions. This practice supports collaboration and enhances project maintainability, which are key for data engineering teams working in dynamic environments. The other methods, while potentially useful in certain contexts, do not inherently provide the same level of version control. Local copies of configuration files might not be adequately tracked or managed. Third-party scheduling tools may offer additional functionality, but they may not seamlessly integrate with version control systems. Directly editing configurations in the Databricks interface lacks an inherent mechanism for version control, making it difficult to track changes effectively.