AI-900 dumps materials - exam dumps for AI-900: Microsoft Azure AI Fundamentals

Microsoft Azure AI Fundamentals - AI-900 certification

Exam Code: AI-900

Exam Name: Microsoft Azure AI Fundamentals

Updated: Sep 10, 2026

Q & A: 336 Questions and Answers

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Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.

  • Describe features of conversational AI workloads on Azure (15-20%)
  • Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
  • Describe fundamental principles of machine learning on Azure (30-35%)
  • Describe AI workloads and considerations (15-20%)
  • Describe features of computer vision workloads on Azure (15-20%)

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

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The individuals can pursue a lot of career opportunities with a Microsoft certificate. The Microsoft Certified: Azure AI Fundamentals certification is not an exception. Although it is a foundational-level path, the certificate holders can start their careers on a good footing. Some of the job titles that these entry-level professionals can apply for include an Azure Data Scientist, an Azure AI Engineer, an Azure Administrator, an Azure Developer, an Azure Solution Architect, and an Azure DevOps Engineer. For these positions, the applicants can expect to earn an average of $80,000 per annum.

Microsoft AI-900 Exam Syllabus Topics:

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

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