Exam AI-900: Microsoft Azure AI Fundamentals

This exam assesses foundational understanding of artificial intelligence concepts and how they map to Microsoft Azure services, focusing on practical recognition of workloads, core machine learning ideas, and responsible AI considerations rather than deep programming or data science expertise. Candidates should be able to identify common AI problem types—such as regression, classification, clustering, image classification, object detection, optical character recognition, entity extraction, sentiment analysis, translation, speech recognition and synthesis, and generative tasks—and match those problems to appropriate Azure capabilities. The guide emphasizes essential competencies: distinguishing features of major AI workloads, basic machine learning lifecycle concepts (features vs. labels, training/validation splits, performance metrics), high-level differences between classical ML and deep learning, and the role of Transformer architectures in language and generative solutions. It also outlines platform-specific knowledge candidates should know at a conceptual level, including Azure Machine Learning for model development, training and deployment, Azure AI Vision and Face services for computer vision, Azure AI Language and Speech services for NLP and speech, and Azure OpenAI / Azure AI Foundry for generative scenarios and managed model catalogs. Practical skills covered include recognizing when automated machine learning is appropriate, understanding model management and deployment capabilities, and selecting data and compute options for data science work in Azure. Cross-cutting themes that the exam explicitly evaluates are responsible AI and solution quality: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability, with an expectation that candidates can describe considerations and trade-offs for designing AI solutions that meet these principles. Scope boundaries are clear: the exam tests conceptual understanding and ability to choose and reason about services and solution types rather than expecting candidates to implement full production pipelines or advanced statistics. The intended audience includes technical and non-technical professionals who need a structured baseline in AI on Azure; prior experience with basic cloud concepts and client–server applications helps but data science or software engineering experience is not required. Preparation is best done by studying the listed skills measured, using Microsoft Learn modules and hands-on labs to solidify the ability to recognize scenarios, map them to services, and articulate responsible AI considerations and basic lifecycle practices.