Microsoft AI-900 : Microsoft Azure AI Fundamentals

  • Exam Code: AI-900
  • Exam Name: Microsoft Azure AI Fundamentals
  • Updated: Aug 17, 2026     Q & A: 336 Questions and Answers

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Exam Topics

The potential candidates for this certification test must develop their skills in its topics before attempting it. It is recommended to go through the official webpage to review the detailed objectives. Microsoft updates the topics of its exams regularly, so studying the latest domains is very important to your success in the test. Thus, Microsoft AI-900 covers the following sections:

Describe AI Considerations and Workloads (15-20%)

The first topic evaluates the following skills:

  • Identify the guiding principles associated with responsible Artificial Intelligence – This one will evaluate the candidates’ ability to explain some concepts. These include considerations of safety and reliability in AI solutions and considerations for fairness in AI solutions. It also covers considerations for security and privacy, transparency, inclusiveness, and accountability within an AI solution.
  • Identify the common AI Workloads features – You should be able to identify a variety of workloads, such as forecasting/prediction, computer vision, and conversational AI. The subsection also covers the characteristics of anomaly detection workloads, knowledge mining, and natural language processing workloads.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

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The Microsoft AI-900 exam is designed for those individuals who have little to no experience in the world of IT. It is aimed at the students with both non-technical and technical backgrounds. They have basic programming experience and knowledge. However, they are not required to have software engineering or data science experience.

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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Describe features of Generative AI workloads on Azure15-20%- Describe Azure OpenAI Service capabilities
- Identify responsible AI considerations for generative AI
- Describe generative AI concepts
Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Identify Azure AI services for NLP
- Identify common NLP tasks
- Describe Azure capabilities for NLP
Describe fundamental principles of machine learning on Azure30-35%- Describe features of no-code automated ML
- Describe core machine learning concepts
- Identify common machine learning tasks
- Describe Azure Machine Learning capabilities
Describe features of computer vision workloads on Azure15-20%- Identify Azure AI services for computer vision
- Identify common computer vision tasks
- Describe Azure capabilities for computer vision
Describe AI workloads and considerations15-20%- Identify guiding principles for responsible AI
- Identify features of common AI workloads

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