Directory / Databricks

Databricks Certified Machine Learning Professional

Enterprise-scale ML and MLOps on Databricks, including distributed training, advanced MLflow and deployment testing. It has no generative AI content.

What it signals

Practical assessment: Scenario-based multiple choice. Proctored multiple-choice, scenario-based with code.
Identity assurance: Proctored with identity verification. Proctored; exam guide says online proctored, web page says online or test centre. No test aides.
Depth: Professional. Professional level.
Experience expected: About 1 year. 1+ year recommended.
Portability: Mixed. MLOps concepts transfer; implementation details are Databricks-specific.
Verifiability: Public badge or registry. Public Accredible badge.

For hiring managers

Rewritten in Sept 2025 (three sections instead of the older four-domain blueprint). Strong MLOps signal, no generative AI. Same $200 price as associate exams.

Verifying it

Digital badge issued via Accredible at credentials.databricks.com (shareable URL an employer can open); holders may also opt in to the Databricks Certified Directory.

What it covers

Published exam outline

Model Development 44%
MLOps 44%
Model Deployment 12%

By topic

GenAI apps: Not covered. No foundation-model content.
Agents: Not covered. No agent content.
Classical ML: Core focus (over 35%). Model Development is 44%.
Operations: Core focus (over 35%). MLOps 44% plus Model Deployment 12%.
Data: Some (10 to 20%). Feature Store and streaming feature pipelines are a sub-part of Model Development (estimated 10-20%).
Responsible AI: Not covered. Not in blueprint.

Exam details

Who the provider says it is for. ML engineers who design, implement and manage enterprise-scale ML on Databricks: SparkML pipelines, distributed training/tuning, advanced MLflow, Feature Store, MLOps testing, Asset Bundles, Lakehouse Monitoring, deployment strategies.

Format: Multiple choice (59 scored)
Delivery: Proctored; exam guide says online proctored, web page says online or test centre. No test aides.
Code or hands-on work: Code/API reading in multiple-choice items (Python, SparkML, MLflow, Optuna, Ray). No labs.
Prerequisites: None
Recommended experience: 1+ year hands-on experience with the ML tasks in the exam guide
Renewal: Retake the full current exam

Preparing

Not confirmed

These details were not available from an official source at the time of research.

passingScore (not published)

Sources (3)