Quick tips
- 60/30/10 split: labs/theory/quizzes.
- Use glossary‑based flashcards.
- Spread study over 2–4 weeks.
AI‑900 Checklist
Tick objectives, save progress and open quick references from your Semantic Content Network.
Resources · Quick links: AI‑900 guide · Study plan · Simulators · Glossary
Progress is saved locally in your browser (localStorage).
Domains & objectives
| ✓ | Objective | Quick refs |
|---|---|---|
| AI fundamentals: model types, LLMs vs classic models, scenarios. | AI‑900 guide · Glossary LLM | |
| Responsible AI: fairness, privacy, security, governance. | Integration & governance | |
| Vision: classify, detect objects, OCR, image moderation. | Computer Vision | |
| Language: text classification, NER, sentiment, Q&A, RAG (concepts). | Language · RAG | |
| Speech: speech‑to‑text, text‑to‑speech, translation. | Speech | |
| Decision: recommendations and anomaly detector (concepts). | Decision | |
| Azure Machine Learning: studio, pipelines, datasets, basic endpoints. | Azure ML | |
| Hands‑on labs: at least 2 guided exercises + 1 mini project. | Study plan | |
| Quizzes & simulators: 2 full sessions with error review. | Simulators | |
| Ready‑check: score ≥ 80% on mocks + glossary mastery. | Glossary |
Quick questions
Do I need Azure experience?
For AI‑900, conceptual familiarity is enough; labs will guide you.
When should I book?
Once you consistently pass the 80% ready‑check.