Exam Code: AI-102J
Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
Updated: Sep 16, 2026
Q & A: 425 Questions and Answers
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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
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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Process and index video content - Analyze images and detect objects/features - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models |
| Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Build knowledge bases and search indexes |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models |
| 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 |
| Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models |
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