Review Microsoft Azure AI Fundamentals (AI-900) workloads, Azure service families, AI concepts, and common service-selection traps before practicing in IT Mastery.
AI-900 rewards clear workload recognition. Use this cheat sheet to separate machine learning, computer vision, natural language processing, generative AI, and responsible AI concepts before you answer timed practice questions.
Use this with practice. Review the workload distinctions, then try the free AI-900 diagnostic or open the full IT Mastery practice bank.
| Field | Detail |
|---|---|
| Issuer | Microsoft |
| Exam name | Microsoft Azure AI Fundamentals |
| Exam code | AI-900 |
| IT Mastery status | Live AI-900 practice available |
| Transition note | Check Microsoft Learn before exam day because AI-900 has a published transition path toward AI-901. |
| Domain | Practice weight | What to know | Common trap |
|---|---|---|---|
| AI workloads and considerations | 19% | Responsible AI principles, prediction, classification, anomaly detection, and common workload categories | Memorizing service names without recognizing the workload |
| Machine learning on Azure | 19% | Training, evaluation, regression, classification, clustering, and Azure Machine Learning basics | Confusing custom model training with prebuilt AI services |
| Computer vision on Azure | 19% | Image analysis, object detection, OCR, facial recognition concepts, and vision inputs | Choosing text analytics for image or video problems |
| Natural language processing on Azure | 19% | Sentiment, key phrases, entities, translation, speech, and language understanding | Missing whether the input is speech, text, image, or document layout |
| Generative AI workloads | 24% | Prompts, completions, summarization, content generation, grounding, and responsible use | Treating generative AI as the answer to every AI scenario |
| Distinction | How to decide |
|---|---|
| Classification vs regression | Classification predicts a category; regression predicts a numeric value. |
| Clustering vs classification | Clustering groups unlabeled data; classification uses labeled examples. |
| Computer vision vs OCR | Vision interprets images; OCR extracts text from images or documents. |
| Speech-to-text vs text translation | Speech-to-text transcribes audio; translation changes text from one language to another. |
| Sentiment vs key phrases | Sentiment detects opinion; key phrases extract important terms. |
| Generative AI vs extraction | Generative AI creates new content; extraction identifies existing facts or fields. |
| Responsible AI fairness vs privacy | Fairness addresses bias and equitable treatment; privacy protects personal or sensitive data. |
| Azure Machine Learning vs Azure AI services | Azure Machine Learning supports custom ML workflows; Azure AI services provide prebuilt capabilities. |
Start with the free AI-900 diagnostic and group misses by workload type. If most misses are service-selection errors, drill the topic pages until the input/output pattern is clear. If most misses are AI-concept errors, review the ML and responsible AI distinctions before returning to mixed practice.
Because AI-900 is a fundamentals route, avoid overtraining on memorized stems. The target is fast recognition of the workload, the Azure service family, and the reason the distractors do not fit.