Answer: Use databricks jobs update with a JSON payload dynamically generated by an external application to adjust schedules.

Option B is the most suitable approach for dynamically adjusting Databricks job schedules based on external triggers using the Databricks CLI. Here‘s why: 1. **Automation**: The Databricks CLI with a JSON payload allows for automation, enabling external triggers to automatically generate the necessary JSON payload to update job schedules without manual intervention. 2. **Flexibility**: Dynamically generating the JSON payload offers the flexibility to adjust job schedules based on various external triggers or events, creating a more responsive and adaptable scheduling system. 3. **Efficiency**: Manual updates via the Databricks UI (Option C) can be time-consuming and error-prone. Using the CLI with a JSON payload streamlines the process and reduces human error. 4. **Integration**: Integrating the Databricks CLI with an external application to generate the JSON payload ensures seamless communication between systems, allowing job schedules to be adjusted in real-time based on external events. 5. **Scalability**: While implementing a continuous integration pipeline (Option A) may be complex for adjusting job schedules, using the Databricks CLI with a JSON payload provides a straightforward and scalable solution for dynamic scheduling. In conclusion, Option B efficiently automates the process of dynamically adjusting Databricks job schedules based on external triggers using the Databricks CLI.

Author: LeetQuiz Editorial Team