Databricks Databricks-Machine-Learning-Professional Exam Details & Actual Exam Questions

  • Exam Code/Number: Databricks-Machine-Learning-Professional
  • Exam Name/Title: Databricks Certified Machine Learning Professional
  • Certification Provider: Databricks
  • Corresponding Certification: ML Data Scientist
  • Exam Questions: 193
  • Updated On: Jun,26 2026
  • Certification Level: Professional

Databricks Certified Machine Learning Professional Exam Questions

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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Machine Learning Professional
Exam Number:Databricks-Machine-Learning-Professional
Passing Score:Not publicly disclosed
Exam Price:$200 USD
Available Languages:English
Exam Duration:120 minutes
Real Exam Qty:Approximately 45–60
Certificate Validity Period:2 years
Exam Format:Multiple choice, Multiple select, Scenario-based questions
Related Certifications:Databricks Certified Machine Learning Associate
Sample Questions:Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure)
Pre Condition:Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Official Syllabus URL:https://www.databricks.com/learn/certification

Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

SectionObjectives
Model Deployment and Serving- Model deployment strategies
  • 1. Databricks Model Serving
    • 2. Batch inference vs real-time inference
      MLflow and Experiment Tracking- Experiment management
      • 1. Model comparison and selection
        • 2. Tracking runs and parameters
          - Model registry
          • 1. Versioning and lifecycle management
            Machine Learning Models and Algorithms- Unsupervised learning methods
            • 1. Clustering techniques
              • 2. Dimensionality reduction
                - Supervised learning methods
                • 1. Model evaluation metrics
                  • 2. Classification and regression models
                    MLOps, Monitoring, and Governance- Model monitoring
                    • 1. Performance tracking in production
                      • 2. Data drift detection
                        - Governance and compliance
                        • 1. Feature Store usage and management
                          • 2. Model lifecycle governance
                            Machine Learning Workflow on Databricks- Data preparation and feature engineering
                            • 1. Feature engineering techniques
                              • 2. Data ingestion and cleaning in Databricks
                                - End-to-end ML pipelines
                                • 1. Pipeline construction and orchestration
                                  • 2. Reusable ML workflows


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