Databricks Learning Path

The Databricks Certified Data Analyst Associate exam evaluates a candidate's proficiency with the Databricks Data Intelligence Platform, assessing their ability to manage data with Unity Catalog.

The Databricks Certified Data Engineer Associate exam evaluates a candidate’s proficiency with the Databricks Data Intelligence Platform, assessing their ability to build and manage data pipelines, perform ETL using Spark SQL or PySpark, transform and process data, and deploy, schedule, and monitor workloads while applying basic data governance and quality practices.

The Databricks Certified Data Engineer Professional exam assesses advanced skills in designing, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform, including secure ETL pipelines, batch and streaming processing with Python and SQL, and workflow orchestration using core Databricks tools and services.

The Databricks Certified Generative AI Engineer Associate exam assesses the ability to design and implement LLM-enabled solutions using Databricks. This includes breaking down complex problems, selecting appropriate models and tools from the generative AI landscape, and developing end-to-end AI solutions. The exam also covers Databricks-specific tools such as Vector Search for semantic similarity, Model Serving for deployment, MLflow for lifecycle management, and Unity Catalog for governance. Candidates are expected to build and deploy performant RAG applications and LLM chains using the Databricks platform.

The Databricks Certified Machine Learning Associate exam assesses foundational skills in using Databricks to perform basic machine learning tasks, including data exploration and feature engineering, model training, tuning and evaluation, leveraging tools such as AutoML, Unity Catalog, and MLflow, and deploying machine learning models using the Databricks platform.

The Databricks Certified Machine Learning Professional exam assesses advanced skills in using Databricks to design, implement, and manage enterprise-scale machine learning solutions. This includes building scalable ML pipelines with SparkML, performing distributed training and hyperparameter tuning, leveraging advanced MLflow features, and utilizing Feature Store concepts for automated feature pipelines.