The optimal approach for monitoring data pipeline health in Databricks, encompassing both performance metrics and data quality, involves: C. Leverage MLflow for monitoring job performance and integrate Apache Griffin for data quality checks.

1. MLflow: An open-source platform designed for the end-to-end management of the machine learning lifecycle. It offers tracking capabilities to monitor and compare experiment runs, enabling the tracking of job performance metrics like execution time, resource utilization, and model accuracy. Utilizing MLflow facilitates the monitoring of data pipeline performance, helping to pinpoint bottlenecks or issues.

2. Apache Griffin: A data quality solution that enables the definition and enforcement of data quality rules and checks within data pipelines. Integrating Apache Griffin into your Databricks setup allows for the establishment of data quality checks, ensuring the accuracy and reliability of data pipeline outputs. This early identification of data quality issues aids in maintaining data integrity.

Combining MLflow for performance monitoring with Apache Griffin for data quality checks provides a holistic monitoring solution, offering insights into both the efficiency and the quality of data pipelines, thereby ensuring smooth operations and high-quality results.