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  • Exam Code: AI-100日本語
  • Exam Name: Designing and Implementing an Azure AI Solution (AI-100日本語版)
  • Updated: Jul 23, 2026
  • Q & A: 163 Questions and Answers
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  • Exam Code: AI-100日本語
  • Exam Name: Designing and Implementing an Azure AI Solution (AI-100日本語版)
  • Updated: Jul 23, 2026
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  • Exam Code: AI-100日本語
  • Exam Name: Designing and Implementing an Azure AI Solution (AI-100日本語版)
  • Updated: Jul 23, 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:

  • 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.
  • Understanding when a custom API should be developed to satisfy certain business requirements.
  • Expertise in implementing and designing AI applications and agents that utilize Microsoft Azure Cognitive Services.
  • Familiarity with data storage in Azure, Azure Cognitive Search, Microsoft Azure Cognitive Services, and Azure Bot Service.

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

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Microsoft AI-100日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Analyze Solution Requirements25-30%- Analyze AI workloads
  • 1. Determine data requirements
  • 2. Select appropriate Azure AI services
  • 3. Identify business requirements
Topic 2: Implement and Monitor AI Solutions25-30%- Implement AI services
  • 1. Configure security and authentication
  • 2. Integrate AI APIs and SDKs
  • 3. Deploy Azure Cognitive Services
- Monitor and optimize solutions
  • 1. Performance monitoring
  • 2. Logging and diagnostics
  • 3. Model and service optimization
Topic 3: Design AI Solutions40-45%- Design natural language processing solutions
  • 1. Text analytics solutions
  • 2. Translation solutions
  • 3. Language understanding solutions
- Design computer vision solutions
  • 1. OCR and text extraction solutions
  • 2. Image classification solutions
  • 3. Object detection solutions
- Design conversational AI solutions
  • 1. Knowledge integration
  • 2. Bot architecture
  • 3. Conversational workflows

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