1Design and Prepare a Machine Learning Solution

• 1.1Azure Machine Learning Workspace and Resources

  Understand the core ML platform Azure Machine Learning (Azure ML): the workspace, compute (instances/clusters), datastores/data assets, and dev surfaces (Studio, SDK, CLI). The starting point for DP-100.

• 1.2The ML Process and Responsible AI

  Understand the ML lifecycle (data prep → train → evaluate → deploy → monitor), choosing among Automated ML (AutoML), the Designer, and code (SDK/CLI), plus Responsible AI (fairness, explainability).

• 1.3Managing Security, Cost, and Compute

  Understand Azure ML operations—authentication/access (Entra/RBAC, managed identity), secrets (Key Vault), networking, compute cost management, and quotas. Operate ML securely and efficiently.