Azure AI-900 Exam Topics and Skills Measured
What's on the Azure AI Fundamentals (AI-900) exam — the five skill areas, their weightings, and what each really tests — in plain language.
AI-900 is the friendliest way into Azure AI — no coding and no data-science background required. Knowing the five skill areas tells you where to focus.
The five skill areas at a glance
AI-900 is organised into five skill areas of roughly equal size (each 15-20% of the exam). Because the weightings are so even, the exam rewards broad familiarity across all five rather than deep expertise in any one. The newest addition is generative AI, added when Microsoft refreshed the exam to reflect large language models.
| Skill area | Weight | What it really tests |
|---|---|---|
| 1. AI workloads and considerations | 15-20% | Recognising the common types of AI workload (prediction and machine learning, computer vision, natural language processing, document processing, and generative AI) and the principles of responsible AI. |
| 2. Machine learning fundamentals | 15-20% | Core machine-learning concepts — features and labels, regression, classification, and clustering — and the capabilities of Azure Machine Learning (automated ML and the designer). |
| 3. Computer vision workloads | 15-20% | Image classification, object detection, optical character recognition (OCR), and facial detection, and the Azure AI Vision services that provide them. |
| 4. Natural language processing (NLP) | 15-20% | Key phrase extraction, entity recognition, sentiment analysis, language detection, translation, and speech — and the Azure AI Language and Speech services. |
| 5. Generative AI workloads | 15-20% | What generative AI is, language and image generation, large language models at a high level, and the Azure OpenAI Service and Azure AI Foundry capabilities. |
The six Microsoft responsible AI principles — fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability — sit inside skill area 1 but show up throughout the exam. Learn them by name and by example; they are among the most reliable marks.
How the exam is structured
Microsoft does not publish a fixed question count, but AI-900 is typically 40-60 questions in about 45 minutes. Scores are reported on a 100-1000 scale, and 700 or above passes. There are no prerequisites and no coding is required. Treat any practice percentage as a study signal, not a predicted exam score.
AI-900 is one of Microsoft's fundamentals exams, alongside AZ-900 (Azure cloud fundamentals) and DP-900 (data fundamentals). They don't overlap much — AI-900 is the one focused on artificial intelligence, machine learning, and generative AI concepts. Many learners take AZ-900 first for cloud grounding, then AI-900.
- AI-900 has five skill areas, each weighted 15-20% — cover all of them.
- The areas: AI workloads and responsible AI, ML fundamentals, computer vision, NLP, and generative AI.
- The six responsible-AI principles appear throughout the exam.
- Pass is 700 on a 100-1000 scale; no prerequisites and no coding required.
Frequently asked questions
What are the skill areas on the Azure AI-900 exam?
Five, each weighted 15-20%: AI workloads and considerations, machine-learning fundamentals, computer vision, natural language processing, and generative AI.
What is the passing score for Azure AI-900?
700 on a scale of 100 to 1000. The scale is not a straight percentage, so a raw practice percentage does not translate directly to the scaled score.
Do I need coding or data-science experience for AI-900?
No. AI-900 is a fundamentals exam with no prerequisites; it tests concepts and Azure service awareness, not coding or model building.
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