Databricks Certified Machine Learning Associate

The Databricks Certified Machine Learning Associate validates foundational knowledge of machine learning workflows on the Databricks Lakehouse Platform. It covers end-to-end ML lifecycle management using MLflow, Spark ML, and scikit-learn, as well as feature engineering, model training, evaluation, and deployment in Databricks.

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What is the Databricks Certified Machine Learning Associate?

Who Should Take This Course?

Data Scientists building ML models in Databricks
ML Engineers implementing end-to-end ML workflows
Data Engineers adding ML capabilities to data pipelines
Python developers transitioning into machine learning
Analytics professionals working with Databricks ML tools
AI practitioners seeking Databricks-specific ML validation

What You Will Learn in the MLA Course

A comprehensive curriculum covering all exam objectives with hands-on labs and real-world practice.

Databricks ML Platform Fundamentals

Understand the Databricks ML environment and core tools.

Databricks workspace and ML runtime
AutoML for automated model building
Feature Store concepts and usage
Experiment tracking with MLflow

ML Workflows and Experimentation

Build and track ML experiments using MLflow.

MLflow tracking: logging parameters, metrics, artifacts
MLflow Model Registry and model lifecycle stages
Hyperparameter tuning with Hyperopt
Cross-validation and model evaluation

Feature Engineering and Data Preparation

Prepare and engineer features for ML models.

Data cleaning and transformation with PySpark
Spark ML Pipelines for preprocessing
Feature engineering and selection techniques
Handling imbalanced datasets

Model Training and Evaluation

Train, evaluate, and select ML models.

scikit-learn and Spark ML model training
Classification, regression, and clustering models
Model metrics: AUC-ROC, RMSE, F1-score
Distributed model training with Spark

Model Deployment

Deploy models for batch and real-time inference.

Batch inference with Spark UDFs
Real-time serving with MLflow Model Serving
Model versioning and promotion workflows
Monitoring deployed models for drift

Course Prerequisites

Pre-requisites training is free when you purchase the course from ProSupport

Python programming proficiency
Basic understanding of machine learning concepts
Familiarity with Databricks workspace and notebooks
SQL knowledge for data access

Exam Information

Everything you need to know about the MLA certification exam.

Exam Component	Details
Exam Name	Databricks Certified Machine Learning Associate
Exam Code	MLA
Exam Type	Multiple Choice
Total Questions	45
Passing Score	70%
Exam Duration	120 minutes
Language	English
Exam Provider	Databricks / Kryterion
Exam Focus	ML workflows, MLflow, Spark ML, feature engineering, and model deployment on Databricks
Exam Registration	Databricks Academy portal (academy.databricks.com)
Retake Policy	14-day waiting period before retake
Certification Validity	2 years