AI-300 in Practice

About this book

A scenario-driven study guide for candidates preparing for Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions.

It is not a generic product manual. It teaches the production reasoning behind AI-300: how to move machine learning and generative AI systems from experiments into repeatable, secure, evaluated, observable and optimised production workflows.

Across 18 chapters, the book explains how Azure Machine Learning, Microsoft Foundry, MLOps, GenAIOps, prompt versioning, evaluation, observability, RAG optimisation, fine-tuning, deployment safety and lifecycle control fit together in real exam scenarios.

Inside

• A practical AI operations lifecycle lens for reading scenario-based questions
• Azure Machine Learning workspaces, datastores, compute, assets, registries, MLflow, pipelines, model registration, deployment, monitoring and retraining
• Microsoft Foundry, foundation-model deployment, prompt source control, GenAI evaluation, tracing, cost visibility, safety controls and agent observability
• RAG optimisation and fine-tuning decision guidance
• 151 practice questions, a 40-question timed mock exam, a domain-tagged answer key and a weak-spot diagnostic grid
• An appendix of commands, configurations and data shapes to recognise for AI-300

Who it’s for

Cloud, AI and ML engineers, data professionals, solution architects and certification candidates who want exam confidence through production-style reasoning.