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

  • Exam Code/Number: Databricks-Machine-Learning-Associate
  • Exam Name/Title: Databricks Certified Machine Learning Associate Exam
  • Certification Provider: Databricks
  • Corresponding Certification: ML Data Scientist
  • Exam Questions: 76
  • Updated On: Sep,28 2026
  • Certification Level: Associate

Databricks Certified Machine Learning Associate Exam Questions

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Databricks Databricks-Machine-Learning-Associate Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Machine Learning Associate Exam
Exam Number:Databricks-Machine-Learning-Associate
Available Languages:English, Portuguese (Brazil), Korean, Japanese
Exam Price:USD 200 (plus applicable taxes)
Certificate Validity Period:2 years
Passing Score:Not publicly disclosed
Real Exam Qty:48
Related Certifications:Databricks Certified Data Engineer Associate
Databricks Certified Data Analyst Associate
Exam Duration:90 minutes
Exam Format:Multiple-selection, Multiple-choice
Recommended Training:Databricks Academy
Machine Learning with Databricks
Exam Registration:Official Registration
Sample Questions:Databricks Databricks-Machine-Learning-Associate Sample Questions
Exam Way:Online proctored or in-person at authorized test centers
Pre Condition:No formal prerequisites; 6+ months hands-on Databricks ML experience recommended
Official Syllabus URL:https://www.databricks.com/learn/certification/machine-learning-associate

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

SectionWeightObjectives
Databricks Machine Learning38%- Databricks ML Environment and Capabilities
  • 1. AutoML usage
    • 2. Lakehouse for ML
      • 3. Feature Store concepts
        • 4. Unity Catalog for ML assets
          Model Development31%- Training, Tuning and Evaluation
          • 1. Algorithm selection
            • 2. Hyperparameter tuning with Hyperopt
              • 3. Pipeline construction
                • 4. Model evaluation metrics
                  - Data Preparation and Feature Engineering
                  • 1. Handling class imbalance
                    • 2. Feature transformation and selection
                      • 3. Data exploration and cleaning
                        ML Workflows19%- MLflow Tracking and Experimentation
                        • 1. MLflow UI navigation
                          • 2. Run logging and metrics
                            • 3. Experiment management
                              • 4. Artifacts and model logging
                                Model Deployment12%- Model Registry and Serving
                                • 1. Inference and monitoring basics
                                  • 2. Model deployment options
                                    • 3. Model versioning and staging
                                      • 4. Register models in Unity Catalog


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