Who it is for
Designed for AI engineers and data scientists who operationalize traditional machine learning and generative AI workloads on Azure.
AI-300
Operationalizing Machine Learning and Generative AI Solutions
AI-300 validates the skills to design, automate, deploy, monitor, and optimize MLOps and GenAIOps solutions using Azure Machine Learning and Microsoft Foundry.
- Level
- Intermediate
- Format
- Single choice · Multiple choice
- Recommended experience
- Experience with Python, data science, Azure Machine Learning, Microsoft Foundry, GitHub Actions, Bicep, Azure CLI, and entry-level DevOps practices.
- Duration
- 120 min
- Exam data updated
- Mar 5, 2026
Your exam at a glance
What it covers
Assesses MLOps and GenAIOps infrastructure, model lifecycle operations, generative AI evaluation, observability, RAG, and fine-tuning.
What you gain
Passing demonstrates the ability to deliver scalable Azure AI solutions with automated deployment, monitoring, quality assurance, and optimization.
Your learning path to exam readiness
Five clear stepsBeginner Path
Build the Python, Azure ML, Foundry, GitHub Actions, Bicep, and DevOps foundations needed for AI-300.
BeginStudy Plan
Organize the five AI-300 skills areas into a focused MLOps and GenAIOps preparation schedule.
PlanFree Practice Test
Measure your AI-300 knowledge across model lifecycles, Foundry operations, evaluation, and optimization.
PracticeSmart Review
Review weak AI-300 objectives and connect mistakes to the correct Azure AI operations workflow.
ReviewReadiness Check
Check AI-300 skills coverage, hands-on confidence, and exam pacing before scheduling your attempt.
AssessWhat to focus on
Exam blueprint
Design and implement an MLOps infrastructure
19%
Implement machine learning model lifecycle and operations
29%
Design and implement a GenAIOps infrastructure
24%
Implement generative AI quality assurance and observability
14%
Optimize generative AI systems and model performance
14%
Keep learning
News for this subject
Oct 15, 2026
AI-300: Certification Scope and the AI Operations Role
Introduction
Oct 15, 2026
AI-300: Designing Workspaces, Compute, Assets, and Registries
Design Azure Machine Learning around environment-specific workspaces and explicitly versioned reusable assets.
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AI-300: Securing Azure Machine Learning Access and Networks
Secure Azure Machine Learning access by designing authorization and connectivity for each request separately. Microsoft Entra ID identifies the principal; Azure role-based access control (RBAC) governs allowed resource operations.