Microsoft AI-102日本語 : Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)

Exam Code: AI-102J

Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)

Updated: Sep 13, 2026

Q & A: 425 Questions and Answers

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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

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Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam

Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.

This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.

It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.

After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.

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

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