Operationalizing Machine Learning and Generative AI Solutions Study Guide

Preparing for AI-300, Operationalizing Machine Learning and Generative AI Solutions? You are in the right place. This is the written companion to the practice exams, a complete walk through everything the test covers.

AI-300 is an operations exam, and it rewards judgment over recall. Most questions are short scenarios where two answers both technically work, and the better one follows one instinct that runs through the whole test: prefer the managed, least-privilege, reproducible option over the one you wire by hand, grant broadly, or click together in the portal, unless a stated hard constraint overrides it. So you weigh a managed online endpoint against self-hosting your own Kubernetes cluster, or, when a generative app underperforms, retrieval against fine-tuning, since retrieval fixes what the model knows while fine-tuning fixes how it behaves. Often the first move is naming the lifecycle stage or observability pillar the question lives in, and the tool follows.

The guide follows the five official domains, weighted the way the real exam weights them, from the heavily weighted model lifecycle domain at 25 to 30 percent to the lighter generative AI domains at 10 to 15 percent each. Each chapter builds the mental model in plain language, separates the look-alike options with comparison tables and decision trees, and ends with a cheat sheet. Start at the top, or pick a domain from the list beside this page.