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Exam Information
Official specifications published by Databricks
Exam Format
120 min
60
Not specified
Professional
Registration
$200 USD
Kryterion Online Proctoring or online proctoring
Validity
2 years
Take the current version of the exam
ML-PRO Exam Topics and Domains
ML-PRO is organized into 3 weighted domains. Expect to work with Unity Catalog, MLflow, Lakehouse Monitoring, Model Serving, and more.
1
Model Development
35%
Using Spark ML
SparkML FundamentalsBatch and Streaming Inference
- Identify when SparkML is recommended based on requirements
- Construct ML pipelines using SparkML
- Apply appropriate estimator/transformer for use cases
- Tune SparkML models using MLlib
- Evaluate SparkML models
- Score SparkML models for batch/streaming use cases
Scaling and Tuning
Distributed TrainingHyperparameter Tuning
- Scale distributed training pipelines using SparkML and pandas APIs
- Perform distributed hyperparameter tuning with Optuna/Ray
- Evaluate scaling trade-offs
- Select appropriate parallelization strategies
- Compare Ray and Spark for ML workloads
Advanced MLflow Usage
Complex Experiment TrackingCustom Model Objects
- Utilize nested runs for complex experiments
- Log custom metrics, parameters, and artifacts
- Create custom model objects with feature engineering
- Understand MLflow flavors and pyfunc benefits
Advanced Feature Store Concepts
Feature Store OperationsOnline Feature Serving
- Ensure point-in-time correctness in feature lookups
- Build automated feature computation pipelines
- Configure online tables for low-latency applications
- Design scalable streaming feature solutions
- Develop on-demand features for consistent use
2
MLOps
35%
Model Lifecycle Management
Lifecycle Architecture
- Describe and implement model lifecycle pipeline architecture
- Map Databricks features to lifecycle management process
Validation Testing
Testing Strategies
- Implement unit tests for Databricks notebooks
- Identify testing types for various environments
- Design integration tests for ML systems
Environment Architectures
Databricks Asset Bundles (DABs)
- Design scalable Databricks environments using best practices
- Configure ML assets using Databricks Asset Bundles
Automated Retraining
Retraining Workflows
- Implement automated retraining workflows
- Develop strategies for model selection during retraining
Drift Detection and Lakehouse Monitoring
Statistical Drift DetectionMonitoring Pipelines
- Apply statistical tests for drift detection
- Build monitors for different table types
- Configure alerting mechanisms
- Evaluate model performance trends
- Monitor endpoint health metrics
3
Model Deployment
30%
Deployment Strategies
Advanced Deployment Patterns
- Compare deployment strategies and evaluate suitability
- Implement model rollout strategies using Databricks Model Serving
Custom Model Serving
Custom PyFunc Models
- Register custom PyFunc models with Unity Catalog
- Query custom models via REST API or MLflow Deployments SDK
- Deploy custom model objects using various methods
How do I earn this certification?
Passing ML-PRO earns the Databricks Certified Machine Learning Professional certification. It sits in the Machine Learning track.
Current Level Exams
databricks-data-engineer-professional - Data Engineer ProfessionalComplementary data engineering expertise
Next Level Options
databricks-solutions-architect - Databricks Solutions Architect Advanced architecture design for ML systems
Alternative Paths
- databricks-genai-engineer-associate - Databricks Certified Generative AI Engineer Associate Emerging AI specialization with LLMs and GenAI
- databricks-data-analyst-associate - Databricks Certified Data Analyst AssociateSQL and analytics skills for ML insights
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
Use the official PlanetCert Practice Test alongside the study plan below to prepare efficiently for ML-PRO.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2025-09-30
- Announcement date: 2025-08-01
Updates
- SparkML Latest in DBR 15.4 LTS ML Now major focus area in updated exam • Release date: 2025-08
- Ray 2.x Added for distributed hyperparameter tuning • Release date: 2025
- Databricks Asset Bundles (DABs) GA New requirement for environment management • Release date: 2025
- Lakehouse Monitoring GA Critical for drift detection topics • Release date: 2025
Who should take this exam?
This exam is typically taken by Machine Learning Engineers and Data Scientists (Advanced).
- 1+ years hands-on experience with Databricks Machine Learning
- Working knowledge of Python and ML libraries (scikit-learn, SparkML, MLflow)
- Experience with Lakehouse Monitoring and Databricks Model Serving
- Completion of Machine Learning at Scale course
- Completion of Advanced Machine Learning Operations course