Microsoft AI-300 Certification Preparation Guide 2026 — Complete Study Plan & Resources

The Microsoft AI-300 certification validates your ability to operationalize machine learning and generative AI solutions on Azure. This guide covers everything you need to pass the exam, including the official Microsoft Learn modules, study strategies, and practice resources.

AI-300 Exam Overview

Skills Measured (Exam Weightage)

Official Microsoft Learn Modules (Free)

The following Microsoft Learn modules are the official preparation materials, organized by exam domain:

🟦 Domain 1: Design and Plan AI Solutions (15-20%)

• Get started with AI fundamentals
• Fundamentals of machine learning
• Plan and prepare for GenAIOps
• Azure AI Foundry secure environment
• Implement identity-based security for Azure Machine Learning

🟪 Domain 2: Design and Manage Data Pipelines (20-25%)

• Design a machine learning solution (Learning Path)
• Explore Azure Machine Learning workspace
• Explore Azure ML workspace resources and assets
• Work with environments in Azure Machine Learning
• Work with compute resources in Azure Machine Learning

🟣 Domain 3: Design and Manage Model Training (20-25%)

• Design a machine learning model training solution
• Run a training script as a command job
• Train models with MLflow jobs
• Run training scripts and track models with MLflow
• Perform hyperparameter tuning with Azure ML pipelines
• Optimize and fine-tune agents
• Automate ML model selection (Learning Path)
• Use Azure ML jobs for automation
• Run pipelines in Azure Machine Learning
• Use Azure ML pipelines for automation (Learning Path)

🟢 Domain 4: Design and Manage Deployment Infrastructure (20-25%)

• Deploy and consume models with Azure ML (Learning Path)
• Design a model deployment solution
• Deploy model to batch endpoint
• Continuous deployment for machine learning
• Deploy model with GitHub Actions
• Trigger Azure ML jobs with GitHub Actions
• Trigger GitHub Actions with trunk-based development
• Work with environments in GitHub Actions

🟠 Domain 5: Monitor and Maintain AI Solutions (10-15%)

• Manage and review models in Azure ML (Learning Path)
• Prompt versioning for GenAIOps
• Automated evaluation for GenAIOps
• Optimize generative AI model performance
• Tracing generative AI applications
• Monitor generative AI applications
• Manage and optimize agent investment in Azure
• Introduction to MLOps (Learning Path)
• Design MLOps solution
• Train and deploy ML models (Learning Path)

4-Week Study Plan

Additional Resources

• Official Exam Page: Microsoft Certified: Azure AI Engineer Associate
• Practice Assessment: Microsoft Learn offers a free practice assessment for AI-300
• Azure ML Documentation: Azure Machine Learning documentation
• Responsible AI: Responsible AI practices

Pro Tips for AI-300

1. Hands-on is critical: This exam tests practical Azure ML skills. Create a free Azure account and complete the labs in each Learn module.
2. Focus on GenAIOps: The exam heavily emphasizes generative AI operations — prompt versioning, automated evaluation, tracing, and monitoring.
3. Know the deployment patterns: Understand real-time vs batch endpoints, managed vs Kubernetes deployments, and when to use each.
4. MLflow integration: Know how MLflow integrates with Azure ML for experiment tracking, model registry, and deployment.
5. GitHub Actions for MLOps: The exam tests CI/CD pipelines using GitHub Actions to trigger Azure ML jobs.
6. Security & Identity: Don't skip the security modules — managed identities, RBAC, and network isolation are tested.

FAQs

What score is needed to pass the AI-300 exam?

You need a score of 700 out of 1000 to pass the AI-300 exam. Aim well above this threshold in mocks so exam pressure does not pull you below the line.

What does the scenario style of AI-300 questions look like?

Most questions present a business need followed by constraints around data, cost, latency, or governance. You must choose the Azure AI service or design approach that fits the scenario rather than recalling isolated facts.

How should I split MLOps and GenAIOps in my preparation?

Give solid time to MLOps for pipelines, model tracking, deployment, and monitoring of predictive solutions. Then give focused time to GenAIOps for prompt flow, grounding, evaluation, and lifecycle management of generative solutions.

How much hands on practice is needed compared to theory for AI-300?

Hands on practice matters a lot because scenario judgment improves only after working inside the portal and testing service behavior. Pair short labs with concept revision so theory stays tied to real configuration choices.