Databricks Certified Machine Learning Professional
Databricks' professional certification validates expertise in machine learning on the Lakehouse platform, including feature engineering, model training, and MLflow.
Provider
Databricks
Level
Professional
Duration
2–4 months
Cost
$200
Passing Score
70%
Validity
2 years
Salary Impact
+$20,000–$35,000 avg
Prerequisites
Python programming
Spark fundamentals
ML concepts and workflow
Exam Format
60 multiple-choice questions, 120 minutes, $200 USD
Exam Topics & Weights
Feature engineering and selection
25%
Model training and hyperparameter tuning
20%
Model evaluation and validation
20%
MLflow and model lifecycle management
20%
Databricks platform and Delta Lake
15%
8-Week Study Plan
Weeks 1-2
Focus: Databricks platform and Lakehouse architecture
Databricks Academy courses
Lakehouse whitepaper
Delta Lake documentation
Weeks 3-4
Focus: Feature engineering and data pipelines
Databricks Feature Store
Apache Spark for data processing
SQL for feature creation
Weeks 5-6
Focus: Model training with MLlib and scikit-learn on Spark
MLlib algorithms
Hyperparameter tuning with Databricks
Distributed training patterns
Weeks 7-8
Focus: MLflow for model management and lifecycle
MLflow tracking and experiments
Model registry
Model deployment and serving
Week 9
Focus: Practice exams and Databricks labs
Databricks official practice exam
Databricks Academy hands-on labs
Real ML workflows
Exam Tips & Strategies
Databricks expertise is increasingly valuable—this is a strong differentiator in the job market
MLflow is critical to understand. Know how to log metrics, parameters, models, and artifacts.
Feature engineering accounts for 25% of the exam. This is not just theory—understand best practices.
Understand the Lakehouse concept and how Delta Lake fits into the data ecosystem
Get hands-on with Databricks Community Edition to practice before the exam
Study real Databricks customers' ML workflows to understand practical applications
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