Microsoft AI-102日本語 Q&A - in .pdf

  • Exam Code: AI-102日本語
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
  • Updated: Aug 30, 2026
  • Q & A: 425 Questions and Answers
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  • Exam Code: AI-102日本語
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
  • Updated: Aug 30, 2026
  • Q & A: 425 Questions and Answers
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Who should take the AI-102: Designing and Implementing an Azure AI Solution Exam

The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

Prerequisites

Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.

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Microsoft AI-102日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement natural language processing solutions15-20%- Customize and deploy NLP models
- Implement translation and summarization
- Build conversational AI and chatbots
- Perform text analysis, sentiment detection, and language detection
Topic 2: Implement computer vision solutions10-15%- Process and index video content
- Build and deploy custom vision models
- Extract text and handwriting from images
- Analyze images and detect objects/features
- Integrate vision capabilities into applications
Topic 3: Implement knowledge mining and information extraction solutions15-20%- Ingest and process structured/unstructured data
- Extract entities, relationships, and key phrases
- Build knowledge bases and search indexes
- Implement intelligent search and retrieval
Topic 4: Plan and manage an Azure AI solution20-25%- Create and configure Azure AI resources
- Select appropriate Microsoft Foundry Services
- Select suitable AI models
- Plan solutions aligned with responsible AI principles
- Monitor, optimize, and secure AI solutions
- Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining
Topic 5: Implement an agentic solution5-10%- Build agents with Microsoft Foundry Agent Service
- Understand agent use cases and types
- Develop multi-agent workflows and orchestration
- Test, deploy, and optimize agents
Topic 6: Implement generative AI solutions15-20%- Orchestrate multiple models and containers
- Deploy and manage generative models
- Apply prompt engineering and fine-tuning
- Implement model monitoring and feedback
- Integrate Azure OpenAI and other generative models

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