Microsoft AI-900 Korean exam - in .pdf

AI-900 Korean pdf
  • Exam Code: AI-900 Korean
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
  • Q & A: 336 Questions and Answers
  • PDF Price: $69.99
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  • Exam Code: AI-900 Korean
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
  • Q & A: 336 Questions and Answers
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Microsoft AI-900 Korean exam - Testing Engine

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  • Exam Code: AI-900 Korean
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
  • Q & A: 336 Questions and Answers
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The Microsoft AI-900 exam will measure the candidates’ skills and competence in a range of topics. They are as follows:

  • Explain the Features of NLP (Natural Language Processing) Workloads Available on Azure (15-20%): This subject area will measure your ability to identify the features of basic Natural Language Processing Workload scenarios. It will also test your skills in identifying different Azure services and tools for NLP workloads. The topic will cover the understanding of the capabilities of Text Analytics service, Language Understanding service, Speech service, and Translator Text service.
  • Explain the Fundamental Principles of ML on Azure (30-35%): The potential candidates for the Microsoft AI-900 exam should be able to identify the common types of machine learning and explain its core concepts. They also need to know how to identify the core tasks that are involved in creating the ML solutions. Additionally, they need to have the knowledge of the capabilities of no-code ML with Azure ML studio.
  • Explain the Features of Conversational Artificial Intelligence Workloads Available on Azure (15-20%): The applicants must demonstrate the understanding of common use cases associated with conversational artificial intelligence. This area also measures one’s knowledge of Azure services associated with conversational artificial intelligence.
  • Explain AI Workloads & Considerations (15-20%): This section will measure the individuals’ ability to identify different features of common artificial intelligence workloads. It will also evaluate their competence in identifying the guiding principles that are responsible for AI.
  • Explain the Features of Computer Vision Workloads Available on Azure (15-20%): This domain requires that the test takers demonstrate competence in identifying the basic categories of computer vision solutions. It will also measure their skills in identifying different Azure services and tools for computer vision tasks. You will also need an understanding of the capabilities of Computer Vision service, Custom Vision service, Face service, and Form Recognizer service.

Certification Topics of Microsoft AI-900 Exam

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

  • Describe features of computer vision workloads on Azure (15-20%)
  • Describe AI workloads and considerations (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 features of conversational AI workloads on Azure (15-20%)

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

Who should take the AI-900: Microsoft Azure AI Fundamentals Exam

High level of difficulty and many challenging questions. Identify the different types of AI models. AI design choosing tools. Who should take the exam? Artificial intelligence. Describe the strengths and weaknesses for artificial intelligence.

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Microsoft AI-900 Korean Exam Syllabus Topics:

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

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