Microsoft · 2026 Edition
A complete preparation guide written by Microsoft-certified engineers. Covers the exam format,all 5 blueprint domains, a week-by-week study plan, and proven tips for passing first time.
2–4 weeks
Prep time
Beginner
Difficulty
50
Exam questions
700/1000
Pass mark
Exam code
AI-900
Full name
Microsoft Azure AI Fundamentals
Vendor
Microsoft
Duration
60 minutes
Questions
50 items
Passing score
700/1000 (scaled)
Domains covered
5 blueprint domains
Recommended experience
No prerequisites — suitable for beginners to AI and cloud
Typical prep time
2–4 weeks
AI-900 is Microsoft's AI literacy credential. It is valuable for professionals in data, cloud, development, and management roles who need a shared vocabulary for AI projects and Azure AI services.
Job roles this opens
Official Microsoft blueprint weights — study time should roughly match these percentages.
Week 1
AI Workloads and Responsible AI: principles, use cases, fairness, reliability
Tip: The six Microsoft Responsible AI principles (fairness, reliability/safety, privacy/security, inclusiveness, transparency, accountability) are directly tested. Know each by name and what it means in an AI context.
Week 2
Machine Learning on Azure: supervised vs unsupervised, Azure ML Studio, AutoML
Tip: Know the difference between classification (predict a category), regression (predict a number), and clustering (find groups). Questions give a scenario and ask which ML task type applies.
Week 3
Azure AI Services: Computer Vision, NLP, Document Intelligence, Azure OpenAI
Tip: Azure AI Services are tested by what they do: Computer Vision (analyse images/video), Language (NLP, sentiment), Speech (speech-to-text/text-to-speech), and Azure OpenAI (GPT models). Match the service to the use case.
Week 4
Generative AI: LLMs, prompt engineering, Azure OpenAI, copilots
Tip: Generative AI is a significant addition to AI-900. Know what a large language model (LLM) is, what a foundation model is, and what prompt engineering means. Questions are conceptual — no coding required.
AI-900 is conceptual, not technical. You will not write Python, train models in code, or configure Azure resources — questions test what AI services do and when to use them.
The distinction between AI, machine learning, and deep learning is tested: AI is the broad field, ML is a subset using data-trained models, deep learning is a subset of ML using neural networks.
Know the Azure Machine Learning workspace components at a high level: datasets, experiments, pipelines, models, endpoints. You will not configure them but must identify what each component is used for.
Computer Vision capabilities to know by name: image classification, object detection, optical character recognition (OCR), facial recognition, and spatial analysis. Questions describe an output and ask which capability produced it.
Generative AI on AI-900 covers LLMs, embedding models, image generation, and the concept of grounding responses with your own data (retrieval-augmented generation). These concepts represent a significant portion of the exam.
Apply everything in this guide with adaptive practice questions, detailed answer explanations, and domain analytics.
Deep-dive explanations of the key topics tested on AI-900 — with exam key points and common misconceptions.