AI-300 Study Roadmap

Use the five Microsoft skill areas as the study sequence: MLOps infrastructure, machine learning model lifecycle and operations, GenAIOps infrastructure, generative AI quality assurance and observability, and generative AI optimization. This keeps the roadmap aligned with the current exam title and scope.

Begin with Azure Machine Learning workspaces, compute, environments, datastores, registries, MLflow tracking, model registration, and Managed Online Endpoints. These topics create the operational base for training, deployment, monitoring, rollback, and retraining decisions.

Next, study Azure AI Foundry, Azure OpenAI, Model Catalog, Prompt Flow where applicable, evaluation, tracing, telemetry, token consumption, cost monitoring, and quality metrics. Generative AI operations require review of both application behavior and operational signals.

Use practice after each roadmap block. If retrieval questions are weak, revisit RAG tuning and hybrid search. If deployment questions are weak, revisit Online Endpoints and Managed Online Endpoints. If quality questions are weak, review groundedness, relevance, coherence, fluency, and monitoring workflows.