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Exam AI-900: Microsoft Azure AI Fundamentals

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Part of the requirements for: Microsoft Certified: Azure AI Fundamentals

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Microsoft AI-900 Practice Test Questions, Microsoft AI-900 Exam Practice Test Questions

The individuals who passed the Microsoft AI-900 exam possess the foundational knowledge of artificial intelligence (AI) and machine learning (ML). The test is designed to evaluate the candidates’ understanding of basic AI and ML workloads as well as how to execute them on Microsoft Azure. The students can earn the Microsoft Certified: Azure AI Fundamentals certification at the end of their journey with this path.

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

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Microsoft AI-900 Exam Syllabus Topics:

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

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