Azure Data Scientist Associate (DP-100) Exam Preparation

Launching a career in cloud data science requires a solid grasp of machine learning workflows and the ability to design scalable AI solutions. This text-based course helps you master the core capabilities of Azure Machine Learning and prepares you to tackle the DP-100 certification exam with confidence. You will transition from understanding basic data science principles to configuring, running, and monitoring machine learning pipelines in the cloud. By studying the structured explanations and code examples, you will gain the practical knowledge needed to manage the entire machine learning lifecycle. What you'll learn: - Understand foundational cloud data science concepts, workspace setup, and data asset management. - Configure and run machine learning experiments using the Azure Machine Learning SDK. - Train, optimize, and tune hyperparameters for predictive models. - Deploy models as real-time or batch endpoints for production use. - Apply modern MLOps practices, including model tracking and registry using MLflow. - Explore modern generative AI workloads, prompt engineering basics, and LLM orchestration within the cloud environment. The course starts with essential terminology and workspace configuration before moving into dataset management, model training, and advanced deployment strategies. You will read detailed technical breakdowns and examine real-world code patterns designed to reinforce your learning. This course is designed for aspiring data scientists, cloud developers, and beginners looking to validate their skills with the DP-100 exam, with no prior cloud experience required. Start reading today to build your cloud data science expertise and take the first step toward your professional certification.