Databricks ML Professional Certification: Exam Prep

This course contains the use of artificial intelligence.

This course was produced with the assistance of artificial intelligence. AI tools were used to help draft and structure the lessons, generate the on-screen slides, and synthesize the voice narration. All content has been reviewed and curated by the instructor for accuracy and alignment with the current Databricks Certified Machine Learning Professional exam guide.

Take your Databricks ML skills to production scale, and prove it. This course delivers exam-focused preparation for the Databricks Certified Machine Learning Professional exam: an advanced, MLOps-heavy path through production model development, the ML lifecycle, monitoring, and deployment on the Databricks Lakehouse. It is scenario-driven, often with code, so you learn the decisions the exam actually tests, not theory.

What this course covers, mapped to Databricks' current exam-guide domains:

• Model Development (44%): single-node vs distributed Spark ML training, scaling and tuning with the pandas Function API, Optuna, and Ray, advanced MLflow (autologging, nested runs, signatures, custom PyFunc models), and feature tables with point-in-time correctness using the Feature Engineering client
• MLOps (44%): the model lifecycle with the MLflow Model Registry and models in Unity Catalog, CI/CD and automation with Databricks Asset Bundles and Jobs, automated retraining, testing ML pipelines, and Lakehouse Monitoring for data and model drift (Kolmogorov-Smirnov and chi-square tests, inference tables, and alerting)
• Model Deployment (12%): deployment strategies (blue-green, canary, and shadow), safe traffic shifting on Databricks Model Serving, batch, streaming, and real-time inference, custom PyFunc models, and endpoint health monitoring

How this course prepares you: every topic shows the real Databricks and MLflow workflow and the Python or Spark code as it appears, then frames each the way the exam asks. You will practice the commonly confused pairs it loves: single-node vs distributed training, pandas Function API vs Optuna vs Ray, MLflow Model Registry vs models in Unity Catalog, Kolmogorov-Smirnov vs chi-square drift tests, and blue-green vs canary vs shadow deployment. Two full practice tests and a final exam-simulation chapter build the 60-question, 120-minute timing strategy.

This is a read-and-understand, notebook-centric course. The free Databricks Community Edition or a trial workspace is enough to follow along, and no paid workspace is required. It builds directly on the Databricks Certified Machine Learning Associate and is ideal for experienced data scientists, ML and MLOps engineers, and AI solutions architects.

The exam is 60 questions in 120 minutes, online and remotely proctored, closed-book, with a 70 percent passing score, an approximate fee of USD 200 plus tax, and a two-year validity. Databricks updates the exam guide periodically, so always confirm the current domains and weights on the official Databricks Machine Learning Professional exam page before scheduling.

Enroll today and turn your production Databricks ML expertise into a professional credential.