Pass the actual test with the help of AI-102日本語 study guide
Last Updated: Aug 15, 2026
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| Passing Score | 700 / 1000 |
| Number of Questions | 40-60 |
| Exam Code | AI-102 |
| Exam Price | $165 (USD) |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Duration | 130 mins |
| Schedule Exam | Pearson VUE |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
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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The price of the Microsoft Mobility and Devices Fundamentals exam is $165 USD, for more information related to exam price please visit to Microsoft Training website as prices of Microsoft exams fees get varied country wise.
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| Section | Weight | Objectives |
|---|---|---|
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models |
| 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 |
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Implement model monitoring and feedback |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions |
| Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Extract text and handwriting from images - Build and deploy custom vision models - Integrate vision capabilities into applications |
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