Learn with Microsoft : AI-102日本語 training material for 100% pass
Updated: Jul 27, 2026
No. of Questions: 425 Questions & Answers with Testing Engine
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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.
Exam Details
Speaking of the exam details, the test will contain from 40 to 60 questions of various types which you need to complete within 100 or 120 minutes (depends on the inclusion of labs). To pass the exam you need to schedule the test on the PearsonVUE platform, pay an exam fee which is currently $165, and score at least 700 points or more out of 1000. And, of course, the knowledge of exam topics is a must.
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 |
| Passing Score: | 700 (scaled score out of 1000) |
| Exam Format: | Multiple choice, Multiple select, Drag-and-drop, Case studies, Hot area, Performance-based scenarios |
| Certificate Validity Period: | 1 year (renewable via free online assessment) |
| Exam Duration: | 100 minutes |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals (AI-900) Microsoft Certified: Azure Data Scientist Associate Microsoft Certified: Azure Developer Associate |
| Available Languages: | English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Italian, Indonesian |
| Exam Price: | $165 USD (varies by region: £113 GBP, €126 EUR) |
| Recommended Training: | AI-102T00: Designing and Implementing a Microsoft Azure AI Solution Microsoft Learn Learning Paths for AI-102 |
| 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 |
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots |
| Topic 2: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Analyze images and detect objects/features - Extract text and handwriting from images - Process and index video content - Integrate vision capabilities into applications |
| Topic 3: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Select suitable AI models |
| Topic 4: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |
| Topic 5: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Deploy and manage generative models - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning |
| Topic 6: Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents |
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