Microsoft Azure AI Fundamentals: AI-900 Exam

"Microsoft Azure AI Fundamentals", also known as AI-900 exam, is a Microsoft Certification. With the complete collection of questions and answers, BraindumpsPass has assembled to take you through 336 Q&As to your AI-900 Exam preparation. In the AI-900 exam resources, you will cover every field and category in Microsoft Certified: Azure AI Fundamentals Certification helping to ready you for your successful Microsoft Certification.

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  • Updated on: Sep 10, 2026
  • No. of Questions: 336 Questions & Answers

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  • Updated on: Sep 10, 2026
  • No. of Questions: 336 Questions & Answers

Difficulty in Attempting AI-900: Microsoft Azure AI Fundamentals Exam

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

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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 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.
  • 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.

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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Translator
- Identify types of NLP solutions
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Form Recognizer
- Describe capabilities of Azure Face
- Describe capabilities of Azure Custom Vision
- Identify types of computer vision solutions
Artificial Intelligence workloads and considerations15–20%- Describe considerations for developing AI solutions
- Describe responsible AI principles
- Identify types of AI workloads
Fundamental principles of machine learning on Azure15–20%- Describe core concepts of machine learning
- Describe automated machine learning
- Describe machine learning pipelines
- Describe capabilities of Azure Machine Learning
Features of generative AI workloads on Azure20–25%- Describe generative AI concepts
- Describe capabilities of Azure OpenAI Service
- Describe use cases for generative AI
- Describe responsible AI practices for generative AI

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