How to Prepare for AI-300

Begin with the official AI-300 study guide and list the five skill areas. Then map each area to concrete Azure services: Azure Machine Learning for workspace, training, lifecycle, and endpoint operations; Azure AI Foundry and Azure OpenAI for generative AI apps, agents, evaluation, and Model Catalog scenarios.

Replace fixed calendar plans with flexible study blocks. One block can cover MLOps infrastructure, another model lifecycle and Managed Online Endpoints, another GenAIOps infrastructure, another evaluation and observability, and another optimization. Spend more time on the blocks where practice exposes weak understanding.

Read documentation, then build or inspect artifacts: a workspace, compute resource, environment, experiment run, registered model, online endpoint, evaluation run, and monitoring setup. For generative AI operations, review Prompt Flow, model selection, groundedness metrics, retrieval settings, token use, and cost tracking.

The exam often rewards knowing which operational tool or design choice fits a situation. Managed Online Endpoints are deployment targets. MLflow helps track runs and models. Azure AI Foundry supports generative app and agent workflows. Azure Monitor and Application Insights support observability. These distinctions should guide review.

Practice questions are most useful after study blocks. When you miss a question, write down the exact confusion: workspace setup versus endpoint deployment, model registry versus experiment tracking, evaluation versus monitoring, or RAG retrieval tuning versus fine-tuning. Then return to the specific Microsoft documentation page.