Learn with Microsoft : AI-102日本語 training material for 100% pass

Updated: Sep 07, 2026

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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.

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

Microsoft AI-102日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Designing and Implementing a Microsoft Azure AI Solution
Exam Number:AI-102
Exam Price:$165 USD (varies by region: £113 GBP, €126 EUR)
Certificate Validity Period:1 year (renewable via free online assessment)
Real Exam Qty:40-60
Available Languages:Arabic (Saudi Arabia), Italian, Spanish, English, Portuguese (Brazil), Indonesian, Japanese, French, German, Chinese (Simplified), Korean
Exam Format:Case studies, Multiple choice, Hot area, Multiple select, Drag-and-drop, Performance-based scenarios
Passing Score:700 (scaled score out of 1000)
Related Certifications:Microsoft Certified: Azure Developer Associate
Microsoft Certified: Azure Data Scientist Associate
Microsoft Certified: Azure AI Fundamentals (AI-900)
Exam Duration:100 minutes
Recommended Training:Microsoft Learn Learning Paths for AI-102
AI-102T00: Designing and Implementing a Microsoft Azure AI Solution
Exam Registration:Microsoft Certification Exam Registration
Pearson VUE Scheduling
Sample Questions:Microsoft AI-102日本語 Sample Questions
Exam Way:Online proctored (OnVUE) or onsite at Pearson VUE test centers
Pre Condition:No formal prerequisites; recommended: experience with Azure services, AI concepts, and proficiency in Python or C#; AI-900 certification is recommended but not required
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-102

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.

Microsoft AI-102日本語 Exam Syllabus Topics:

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

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