Azure AI Fundamentals (AI-901) Exam Prep

It is for students and freshers with no AI or Azure background who are preparing for Microsoft Azure AI Fundamentals (AI-901), the successor to the retired AI-900. No programming experience is needed: the course explains concepts and when to use each service rather than how to write production code. It also suits learners from non-computer-science branches, and anyone in a support, sales, product or analyst role who needs to talk accurately about AI workloads and Azure AI services. Because the exam sits at the fundamentals level, the course also works as a plain introduction to modern AI for anyone who wants clear definitions of terms such as embeddings, grounding and agents before moving on to a more technical course.

If you want more depth on machine learning methods after this course, Machine Learning: Foundations covers supervised and unsupervised learning in more detail, and GenAI Engineering: LLM Foundations goes further into how large language models are built. Abekus is independent and is not affiliated with or endorsed by Microsoft; this course is study material written around the published AI-901 skills outline.

Each lesson is a short set of reading cards on one idea, with diagrams where a picture helps. You can read the cards or listen to the narration. The course follows the two AI-901 domains: identify AI concepts and responsibilities, and implement AI solutions by using Microsoft Foundry. It uses current product names throughout, including Microsoft Foundry, Azure AI Vision, Azure AI Speech, Azure AI Language and Document Intelligence, and it explains what each one is for instead of walking through portal click-paths.

A multiple-choice quiz follows every lesson, with an explanation after each answer, and you can ask the AI guide about anything that is still unclear. Questions ask you to identify the AI workload in a scenario, pick the right service, model or metric, or spot a responsible-AI risk. Lessons open in order on a course map.

The final topic sets similar ideas side by side, such as regression versus classification, search versus generation, agents versus chatbots, OCR versus field extraction and training versus inference, then applies them to complete scenarios: supporting phone customers, extracting invoice fields, summarising service calls, searching policy documents and monitoring chatbot quality.

Udemy and Coursera offer AI fundamentals preparation mostly as recorded video lectures, and Whizlabs is known for practice tests for cloud certification exams. Video suits learners who like an instructor to explain each concept, and practice tests suit the last stretch before an exam. This course teaches one idea per short lesson and checks it straight away with a quiz. A fundamentals syllabus is full of similar-sounding terms, such as classification and clustering, grounding and fine-tuning, or OCR and extraction, and a quiz right after each lesson shows whether you can tell them apart while the lesson is still fresh.

Anchor new terms to one example. A support chatbot like the one in lesson one has a language model, a knowledge source it is grounded in, content filters and a set of responsible-AI questions, and much of the syllabus can be placed somewhere in a system like it. When a new concept appears, ask where it would sit in that chatbot.

Next, learn ideas in pairs and know when to choose each: regression or classification, keyword or vector search, grounding or fine-tuning, an agent or a simple chatbot, a prebuilt or custom document model. Learn the six responsible AI principles through examples rather than as a list to memorise: fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability. Use each lesson's quiz as active recall and revisit the lessons where you got questions wrong. Before you book, check Microsoft's current AI-901 skills outline, since exam details can change.