AI-300 Exam Overview

AI-300 covers production operations for traditional machine learning and generative AI systems on Azure. Study Azure Machine Learning workspaces, compute, model lifecycle, Managed Online Endpoints, Azure AI Foundry environments, Azure OpenAI, evaluation, observability, RAG optimization, and model performance tuning.

Microsoft describes the audience as candidates with subject matter expertise in MLOps and GenAIOps on Azure. Useful background includes data science, Python, Azure Machine Learning, Azure AI Foundry, APIs, SDKs, command-line tools, and experience deploying or maintaining models.

Use the official skill areas as the overview: design and implement an MLOps infrastructure, implement machine learning model lifecycle and operations, design and implement a GenAIOps infrastructure, implement generative AI quality assurance and observability, and optimize generative AI systems and model performance. Avoid stating unsourced question counts or exact format details.

Good preparation connects infrastructure to operations. Know how workspace resources, environments, registries, online endpoints, monitoring, evaluation, and rollback decisions affect production reliability. For GenAIOps, focus on Azure AI Foundry, Model Catalog, Prompt Flow, quality metrics, token and cost monitoring, retrieval tuning, and fine-tuning tradeoffs.