Microsoft AI-100日本語 : Designing and Implementing an Azure AI Solution (AI-100日本語版)

  • Exam Code: AI-100日本語
  • Exam Name: Designing and Implementing an Azure AI Solution (AI-100日本語版)
  • Updated: Jul 27, 2026
  • Q & A: 163 Questions and Answers

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Main Prerequisites

Before you decide to take the Microsoft AI-100 exam, it is important that you understand all the knowledge requirements for this test. The exam is not designed for the general public, so you need to possess certain competence before attempting it. The target candidates for this certification test should have the following skills:

  • Familiarity with data storage in Azure, Azure Cognitive Search, Microsoft Azure Cognitive Services, and Azure Bot Service.
  • Expertise in implementing and designing AI applications and agents that utilize Microsoft Azure Cognitive Services.
  • Understanding when a custom API should be developed to satisfy certain business requirements.
  • Ability to put forward solutions that utilize open source technologies with understanding the elements that make up the Azure AI portfolio along with the available data storage options.

Passing the Microsoft AI-100: Designing and Implementing an Azure AI Solution exam is required for anyone who wants to earn the Microsoft Certified: Azure AI Engineer Associate certification. This test evaluates the ability of the candidates to perform the following technical tasks: analyzing solution requirements; designing solutions; integrating AI models into solutions; and deploying and handling solutions.

Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx

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AI-100日本語 exam dumps

What is the duration of the AI-100 Exam

  • Number of Questions: 45-55
  • Length of Examination: 120 minutes
  • Passing Score: 700/1000
  • Format: Multiple choices, multiple answers

Microsoft AI-100日本語 Exam Syllabus Topics:

SectionObjectives
Topic 1: Implement Knowledge Mining and Search Solutions- Azure Cognitive Search
  • 1. Indexing and querying data
    • 2. Skillsets and enrichment pipeline
      Topic 2: Implement Natural Language Processing (NLP)- Text analytics solutions
      • 1. Key phrase extraction
        • 2. Sentiment analysis
          - Language understanding solutions
          • 1. Entity extraction
            • 2. Intent recognition
              Topic 3: Design AI Solutions on Azure- Design conversational AI solutions
              • 1. Azure Bot Service architecture
                • 2. Language understanding (LUIS concepts)
                  - Select appropriate Azure AI services
                  • 1. Knowledge mining solutions
                    • 2. Azure Cognitive Services selection
                      • 3. Azure Machine Learning integration
                        Topic 4: Responsible AI and Monitoring- AI ethics and governance
                        • 1. Model monitoring and evaluation
                          • 2. Responsible AI principles
                            Topic 5: Implement Computer Vision Solutions- Image analysis and processing
                            • 1. Computer Vision API usage
                              • 2. Custom Vision training and deployment

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