Databricks Certified Machine Learning Professional Practice Test

This certification is designed for senior ML engineers, MLOps engineers, and data scientists with at least one year of hands-on experience building and operationalizing machine learning workflows on Databricks. It is appropriate for professionals who work at enterprise scale—managing multi-environment ML deployments, automated retraining pipelines, and production monitoring—rather than those focused solely on model experimentation.

Typical candidates hold roles such as Machine Learning Engineer, MLOps Engineer, Senior Data Scientist, or ML Platform Engineer. Those already holding the Databricks Certified Machine Learning Associate credential who want to demonstrate deeper production-level expertise are also a natural fit for this exam.

There are no formal prerequisites required to register for this exam. However, Databricks strongly recommends at least one year of hands-on experience performing the advanced ML engineering tasks outlined in the official exam guide. Candidates are expected to have practical familiarity with the Databricks platform, Apache Spark and SparkML, MLflow experiment tracking and model registry, and Python-based ML workflows.

Databricks recommends completing the instructor-led courses 'Machine Learning at Scale' and 'Advanced MLOps on Databricks' before attempting the exam. Candidates who hold the Databricks Certified Machine Learning Associate credential will find that foundational knowledge helpful, though it is not a required prerequisite.

The exam consists of 59 scored multiple-choice questions to be completed within 120 minutes. All questions are multiple-choice; there are no hands-on labs or interactive coding tasks. Many questions are scenario-based, presenting real-world Databricks ML workflows and asking candidates to select the most appropriate approach. The exam may also include a small number of unscored items collected for statistical research purposes; these are not identified on the exam form and do not affect the final score, with additional time factored in to accommodate them.

The exam is delivered online through Databricks' exam delivery platform and costs USD $200. A passing score of 70% is required. Certification is valid for two years, after which recertification requires retaking the current version of the exam. The current version of the exam is the September 2025 edition.

• 1.Domain 1 — Model Development (~47%, 22 objectives): Covers building scalable ML pipelines using SparkML estimators, transformers, and pipeline APIs; distributed training with pandas Function APIs and UDFs; hyperparameter tuning at scale using Ray Tune and Optuna; advanced MLflow usage including nested runs, custom metrics, experiment tracking, and model flavors; and Feature Store implementation including automated feature pipelines and point-in-time lookups.
• 2.Domain 2 — MLOps (~43%, 20 objectives): Covers MLOps architecture patterns and multi-environment setups; ML pipeline testing including unit and integration test strategies; Databricks Asset Bundles (DABs) for managing ML assets as infrastructure-as-code; automated retraining workflows with triggers and orchestration; and production monitoring with Lakehouse Monitoring, including configuring drift detection for feature, label, prediction, and concept drift, and setting up alerting on metric degradation.
• 3.Domain 3 — Model Deployment (~10%, 5 objectives): Covers deployment strategies including blue-green and canary rollouts; deploying models via Databricks Model Serving endpoints; building custom model serving solutions with MLflow PyFunc; managing model versions and traffic splitting; and using the REST API and MLflow Deployments SDK for programmatic deployment management.

• Download and study the official Databricks Certified Machine Learning Professional Exam Guide (available at files.training.databricks.com) to understand the exact objectives and their distribution across the three domains before building your study plan.
• Prioritize Lakehouse Monitoring deeply — it appears in approximately 10 exam objectives. Understand all four drift types (feature, label, prediction, concept), the statistical tests used to detect each, and how to configure monitor profiles and alerting in the Databricks UI and via the API.
• Complete the official Databricks instructor-led courses 'Machine Learning at Scale' and 'Advanced MLOps on Databricks', then work through the end-to-end MLOps demo notebooks in the Databricks documentation to see how Asset Bundles, model serving, and monitoring connect in a production workflow.
• Practice with Databricks Asset Bundles (DABs) hands-on using the Databricks Free Edition. Know how to define ML assets (jobs, model serving endpoints, MLflow experiments) in YAML bundle configuration files and deploy them across dev/staging/prod environments.
• Master MLflow's advanced features beyond basic experiment tracking: nested runs for hyperparameter search logging, custom PyFunc model classes, model signatures and input examples, Model Registry stage transitions, and webhook configurations for CI/CD triggers.
• Build fluency with distributed hyperparameter tuning: understand when to use Ray Tune vs. Optuna, how to integrate them with MLflow for run logging, and how SparkML's CrossValidator and TrainValidationSplit compare to these libraries for tuning at scale.
• Use scenario-based practice questions that mirror real Databricks workflows — the exam is heavily applied. Focus on recognizing the correct tool or API for a given production situation rather than memorizing definitions.

Earning this certification positions ML engineers and data scientists for senior and staff-level roles that require end-to-end ownership of production ML systems. Job titles commonly associated with this credential include Senior Machine Learning Engineer, MLOps Engineer, ML Platform Engineer, and AI/ML Architect. Organizations adopting the Databricks Lakehouse Platform at scale—particularly in finance, healthcare, retail, and technology sectors—actively seek professionals who can demonstrate validated expertise in production ML workflows rather than just model development.