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Microsoft AI-102: Designing and Implementing a Microsoft Azure AI Solution Exam is a comprehensive exam that tests your knowledge and skills in designing and implementing AI solutions using Microsoft Azure technologies. AI-102-KR exam is a great way to demonstrate your expertise in AI solutions and is highly valued by employers and clients. With the right preparation, individuals can clear the exam and open up new opportunities in the field of AI solution design and implementation.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
In order to prepare for the AI-102 certification exam, candidates can take advantage of a number of resources provided by Microsoft. This includes online courses, practice exams, and study guides. Additionally, candidates can gain hands-on experience by working on real-world AI projects on the Azure platform. By combining theoretical knowledge with practical experience, candidates can ensure they are fully prepared to pass the exam and demonstrate their skills in designing and implementing AI solutions on Azure.
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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Understand agent use cases and types |
| Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Orchestrate multiple models and containers - Deploy and manage generative models |
| Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization |
| Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Analyze images and detect objects/features - Process and index video content - Extract text and handwriting from images - Build and deploy custom vision models |
| Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |








