Periodically check the metrics through the Databricks UI and manually raise alerts based on observed values, ensuring no additional costs are incurred.

Utilize the Databricks REST API to programmatically collect and analyze metrics, and integrate with an external monitoring tool like Azure Monitor to raise alerts based on predefined thresholds.

Modify the job code to include extensive custom logging for tracking metrics, and use a separate log analysis tool to monitor these logs for anomalies.

Enable autoscaling on the cluster to automatically adjust resources based on workload, assuming it will mitigate all performance-related issues without further monitoring.