Microsoft AI-102日本語 : Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
- Exam Code: AI-102日本語
- Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
- Updated: Sep 04, 2026 Q&As: 425 Questions and Answers
About Microsoft AI-102日本語 Exam
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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.
Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Deploy and manage generative models |
| Topic 2: Implement computer vision solutions | 10-15% | - Process and index video content - Analyze images and detect objects/features - Build and deploy custom vision models - Extract text and handwriting from images - Integrate vision capabilities into applications |
| Topic 3: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models |
| Topic 4: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select suitable AI models - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services |
| Topic 5: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases |
| Topic 6: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service |
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