[NEW] Databricks Certified Machine Learning Associate

About this course

Detailed Exam Domain CoverageDatabricks Machine Learning (38%) Overview of Databricks Machine Learning, Working with AutoML, Using MLflow for tracking, Feature Store, Model RegistryML Workflows (19%) Building pipelines with Databricks Jobs, Orchestrating ML tasks with Delta Lake, Monitoring ML pipelineModel Development (31%) Preparing data for training, Training models, Hyper-parameter tuning, Model evaluation, Model selectionModel Deployment (12%) Register and serve models, Deploy models using MLflow Model Registry, Scaling model serving, Monitoring model performanceI have designed this comprehensive practice test course to help you pass the Databricks Certified Machine Learning Associate exam on your first attempt. Passing this certification requires a solid understanding of how to manage end-to-end machine learning workflows on the Databricks platform. I created these practice questions to mirror the actual exam environment, testing your practical knowledge of Unity Catalog, Feature Store, MLflow, and AutoML. This question bank goes beyond simple memorization by providing detailed explanations for every correct and incorrect answer, ensuring you fully grasp the underlying concepts of model development and deployment. If you are looking for reliable study material to validate your ability to perform fundamental machine learning tasks, this course will serve as your ultimate preparation guide.Practice Questions PreviewQuestion 1: Which of the following Databricks features is specifically designed to log parameters, code versions, metrics, and output files during the machine learning training process?Options:A. Unity CatalogB. Databricks Feature StoreC. MLflow TrackingD. Databricks AutoMLE. Delta Live TablesF. MLflow Model Reg

Course objectives

• Help students pass the Databricks Certified Machine Learning Associate exam on their first attempt
• Provide a solid understanding of end-to-end machine learning workflows on the Databricks platform