AI-300 Practice Test

AI-300 (Operationalizing Machine Learning and Generative AI Solutions) is Microsoft's Associate-level exam for the Machine Learning Operations Engineer certification. It validates the ability to set up infrastructure for machine learning operations (MLOps) and generative AI operations (GenAIOps) on Azure — training, deploying, and monitoring traditional ML models with Azure Machine Learning, and deploying, evaluating, and optimizing generative AI applications and agents with Microsoft Foundry.

The AI-300 exam follows Microsoft's standard Associate-level pricing: $165 USD and a passing score of 700 out of 1000. Duration and exact question count are set by Microsoft at scheduling time and can vary — confirm the current figures on the official Microsoft Learn scheduling page before booking.

The passing score for AI-300 is 700 out of 1000, the same threshold used across nearly all Microsoft role-based certification exams. Microsoft uses scaled scoring, so 700 represents roughly 70% competency across all five exam domains rather than a simple raw percentage.

AI-300 covers five domains: Design and implement an MLOps infrastructure (15-20%) — Machine Learning workspaces, datastores, compute, IaC with Bicep/Azure CLI, and GitHub Actions automation; Implement machine learning model lifecycle and operations (25-30%, the largest domain) — experiment tracking with MLflow, automated ML, model registration, responsible AI evaluation, and production deployment/monitoring; Design and implement a GenAIOps infrastructure (20-25%) — Foundry environments, foundation model deployment, and prompt versioning with Git; Implement generative AI quality assurance and observability (10-15%) — AI quality metrics, risk/safety evaluations, and Foundry monitoring; and Optimize generative AI systems and model performance (10-15%) — RAG tuning and advanced fine-tuning.

AI-300 is a scenario-heavy Associate exam that assumes hands-on MLOps and GenAIOps experience — you are given a realistic production problem (a drifting model, a low-relevance RAG pipeline, a failed rollout) and asked to pick the correct diagnosis and fix, not recall a definition. The generative AI quality and observability domain is the newest and least familiar material for most candidates, since evaluation metrics like groundedness and Foundry-based observability are recent additions to Azure's AI platform.

Candidates already comfortable with Azure Machine Learning, MLflow, and basic Python/DevOps should plan 4-6 weeks, focused mostly on the model lifecycle and GenAIOps domains since together they carry over half the exam weight. Candidates newer to MLOps should plan 10-12 weeks total. We recommend studying the Microsoft Learn path first, then working through 500 scenario-based practice questions with emphasis on model lifecycle operations and generative AI quality assurance.

There are no enforced prerequisites to register for AI-300, but Microsoft designed the exam for candidates with a data science background, Python programming experience, and an entry-level understanding of DevOps practices including GitHub Actions and CLI tooling. Hands-on experience with Azure Machine Learning and Microsoft Foundry is strongly recommended over pure conceptual study.

AI-300 is aimed at MLOps and GenAIOps engineers who set up and maintain the infrastructure and pipelines behind machine learning and generative AI solutions — training pipelines, model registries, deployment endpoints, and Foundry-based agent operations — rather than data scientists building models from scratch or developers only consuming pre-built AI APIs. It pairs well with AI-102/AI-103-style exams that focus on building AI applications, complementing them with the operations and lifecycle management side.