Databricks ML Professional study guide
Databricks ML Professional · Work through your exam, one topic at a time.
The Databricks Certified Machine Learning Professional is a proctored certification exam that assesses a candidate's ability to design, implement, and manage enterprise-scale machine learning solutions using advanced Databricks platform capabilities. It sits above the associate-level ML credential and targets practitioners who already build production ML systems: data scientists and ML engineers working with SparkML pipelines, distributed training and hyperparameter tuning, advanced MLflow features, and Feature Store concepts for automated feature pipelines. The exam also probes MLOps practice - testing strategies, environment management with Declarative Automation Bundles, automated retraining workflows, and drift monitoring with Lakehouse Monitoring - plus deployment topics such as custom model serving and model rollout management. Scoring weight concentrates in two domains: Model Development (44%) and ML Ops (44%), with Model Deployment at 12%. The Databricks ML Professional study guide has 3 sections and about 1,499 words.
01Model Development: Reproducible Training, Features, and Tuning on Databricks
The Model Development domain tests whether you can build a model on Databricks that another engineer can reproduce and a serving system can load. Expect scenario questions on MLflow tracking, Feature Store training sets, hyperparameter tuning, Spark ML pipelines, and model packaging.
504 words, about 3 min02ML Operations: Registry, Promotion, Orchestration, and Monitoring
The ML Operations domain tests the lifecycle after a model trains: versioning, promotion, automation, monitoring, and governance. Questions often describe a model that works but is promoted by overwriting a file or a hard-coded version, then ask what control would have prevented the incident.
497 words, about 2 min03Model Deployment: Batch, Streaming, and Real-Time Serving
The Model Deployment domain tests how you expose a trained model: batch scoring, Structured Streaming, or a real-time Model Serving endpoint. The exam emphasizes the interface between the model artifact and the serving contract, plus safe release patterns.
498 words, about 2 min