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Microsoft AI-900 exam : Microsoft Azure AI Fundamentals

AI-900 Exam Simulator
  • Exam Code: AI-900
  • Exam Name: Microsoft Azure AI Fundamentals
  • Updated: Aug 21, 2026
  • Q & A: 336 Questions and Answers
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Explain Computer Vision Workloads Features on Azure (15-20%)

Here, the following skills will be measured:

  • Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
  • Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.

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

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What is the duration, language, and format of AI-900: Microsoft Azure AI Fundamentals Exam

  • Passing Score: 70%
  • Length of Examination: 50 mins
  • Languages in which this exam is available in: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish
  • Number of Questions: 100 to 120 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice)
  • Type of Questions: This test format is multiple choice

Target Audience

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.

Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
  • 1. Training and validation concepts
    • 2. Supervised vs unsupervised learning
      Topic 2: Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
      • 1. Common AI workloads
        • 2. Responsible AI principles
          Topic 3: Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
          • 1. Sentiment analysis
            • 2. Language modeling and translation
              • 3. Key phrase extraction
                Topic 4: Features of computer vision workloads on Azure15-20%- Computer vision solutions
                • 1. OCR and image analysis
                  • 2. Image classification
                    • 3. Object detection

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