AI-300 study plan

Week 1chapters 1–3 · about 3 hours Done

• Read these chapters:
  • 1. Azure Machine Learning workspaces, assets and compute
  • 2. Automating and securing ML infrastructure
  • 3. Training models and tracking experiments
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Azure Machine Learning workspaces, registries and asset types, Tracking experiments with MLflow in Azure Machine Learning, Hyperparameter tuning with sweep jobs.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Design and implement an MLOps infrastructure · 15–20%Implement machine learning model lifecycle and operations · 25–30%

Week 2chapters 4–7 · about 3 hours Done

• Read these chapters:
  • 4. Registering models, responsible AI and lifecycle
  • 5. Deploying and monitoring machine learning models
  • 6. The Microsoft Foundry platform: resources, identity and networking
  • 7. Deploying foundation models
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Safe rollout and rollback on managed online endpoints, Microsoft Foundry deployment types: Global, Data Zone and Standard.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Implement machine learning model lifecycle and operations · 25–30%Design and implement a GenAIOps infrastructure · 20–25%Implement generative AI quality assurance and observability · 10–15%

Week 3chapters 8–11 · about 3 hours Done

• Read these chapters:
  • 8. Prompt design, variants and version control
  • 9. Evaluation and observability for generative AI
  • 10. Optimising retrieval-augmented generation
  • 11. Fine-tuning foundation models
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Choosing evaluators for generative AI quality and safety.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Design and implement a GenAIOps infrastructure · 20–25%Implement generative AI quality assurance and observability · 10–15%Optimize generative AI systems and model performance · 10–15%

Week 4review Done

• Go back through every chapter’s key takeaways. Anything you can’t explain without looking, re-read that section.
• Re-read the common-trap callouts: they mark the answers that look right but aren’t.
• Check the exam coverage map against Microsoft’s own outline, in case it has changed since the book was checked.
• Take Microsoft’s free practice assessment, if your exam has one, from the official AI-300 study guide page. Revisit the chapters behind any weak area.
• Try the exam sandbox to get used to the exam’s screens and question formats.
• Get the free exam-day checklist and work through it a few days before your exam.

Week 1chapter 1 · about 1 hour Done

• Read chapter:
  • 1. Azure Machine Learning workspaces, assets and compute
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Azure Machine Learning workspaces, registries and asset types.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Design and implement an MLOps infrastructure · 15–20%

Week 2chapter 2 · about 1 hour Done

• Read chapter:
  • 2. Automating and securing ML infrastructure
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Design and implement an MLOps infrastructure · 15–20%

Week 3chapter 3 · about 1 hour Done

• Read chapter:
  • 3. Training models and tracking experiments
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Tracking experiments with MLflow in Azure Machine Learning, Hyperparameter tuning with sweep jobs.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Design and implement an MLOps infrastructure · 15–20%Implement machine learning model lifecycle and operations · 25–30%

Week 4chapters 4–5 · about 2 hours Done

• Read these chapters:
  • 4. Registering models, responsible AI and lifecycle
  • 5. Deploying and monitoring machine learning models
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Safe rollout and rollback on managed online endpoints.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Implement machine learning model lifecycle and operations · 25–30%

Week 5chapters 6–7 · about 1 hour Done

• Read these chapters:
  • 6. The Microsoft Foundry platform: resources, identity and networking
  • 7. Deploying foundation models
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Microsoft Foundry deployment types: Global, Data Zone and Standard.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Design and implement a GenAIOps infrastructure · 20–25%Implement generative AI quality assurance and observability · 10–15%

Week 6chapters 8–9 · about 1 hour Done

• Read these chapters:
  • 8. Prompt design, variants and version control
  • 9. Evaluation and observability for generative AI
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Free study notes for this week: Choosing evaluators for generative AI quality and safety.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Design and implement a GenAIOps infrastructure · 20–25%Implement generative AI quality assurance and observability · 10–15%

Week 7chapters 10–11 · about 1 hour Done

• Read these chapters:
  • 10. Optimising retrieval-augmented generation
  • 11. Fine-tuning foundation models
• After each chapter, cover its key takeaways and explain each one out loud. Re-read any you can’t.
• Look up any unfamiliar term in the AI-300 glossary.

Exam skill areas this week: Optimize generative AI systems and model performance · 10–15%

Week 8review Done

• Go back through every chapter’s key takeaways. Anything you can’t explain without looking, re-read that section.
• Re-read the common-trap callouts: they mark the answers that look right but aren’t.
• Check the exam coverage map against Microsoft’s own outline, in case it has changed since the book was checked.
• Take Microsoft’s free practice assessment, if your exam has one, from the official AI-300 study guide page. Revisit the chapters behind any weak area.
• Try the exam sandbox to get used to the exam’s screens and question formats.
• Get the free exam-day checklist and work through it a few days before your exam.