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Microsoft AI-900 certification exam covers various topics related to AI, including the fundamentals of AI, machine learning, computer vision, natural language processing, and conversational AI. Candidates who pass AI-900-CN exam will demonstrate their understanding of these topics and their ability to apply them in real-world scenarios. The AI-900 exam is an excellent starting point for individuals who want to pursue a career in AI and machine learning.

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

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Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)

In the last section, you will face with the following subtopics:

  • Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
  • Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.

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Microsoft AI-900 中文 Exam Syllabus Topics:

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

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