Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版): AI-102 Exam
"Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)", also known as AI-102 exam, is a Microsoft Certification. With the complete collection of questions and answers, Pass4sureCert has assembled to take you through 425 Q&As to your AI-102 Exam preparation. In the AI-102 exam resources, you will cover every field and category in Azure AI Engineer Associate Certification helping to ready you for your successful Microsoft Certification.
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- Updated on: Aug 16, 2026
- No. of Questions: 425 Questions & Answers
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- Updated on: Aug 16, 2026
- No. of Questions: 425 Questions & Answers
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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Languages: English, Japanese, Chinese (Simplified), Korean
Retirement date: none
This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions; and implement conversational AI solutions.
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 an agentic solution | 5-10% | - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Develop multi-agent workflows and orchestration |
| Topic 2: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization |
| Topic 3: Implement computer vision solutions | 10-15% | - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features - Extract text and handwriting from images - Build and deploy custom vision models |
| Topic 4: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Select suitable AI models - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Topic 5: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |
| Topic 6: Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models |
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