Databricks - Machine Learning Professional

This certification exam assesses a candidate's ability to design, implement, and manage enterprise-scale machine learning solutions on Databricks. The exam tests proficiency in building scalable ML pipelines with SparkML, performing distributed training and hyperparameter tuning with Optuna and Ray, applying advanced MLflow features (nested runs, custom artifacts, custom PyFunc models), and working with advanced Feature Engineering / Feature Store concepts including point-in-time correctness, online tables, and on-demand features. It also evaluates MLOps expertise across model lifecycle management, validation testing (unit and integration), environment architecture with Databricks Asset Bundles, automated retraining, and Lakehouse Monitoring for drift detection, plus model deployment strategies (blue-green, canary), custom model serving via Unity Catalog, and querying via the MLflow Deployments SDK and REST API.