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Microsoft AI-102 exam is designed for professionals who want to validate their skills and knowledge in designing and implementing artificial intelligence (AI) solutions on the Microsoft Azure platform. AI-102-KR exam is intended for developers, data scientists, and AI engineers who want to demonstrate their expertise in building intelligent applications using various Azure services, including Cognitive Services, Machine Learning, and Bot Service.
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To pass the exam, candidates must demonstrate their ability to design and implement AI solutions that meet specific business requirements. They must also be proficient in using Azure services to build intelligent applications that can process natural language, recognize images and speech, and make predictions based on data. Additionally, they must have a clear understanding of how to train and evaluate machine learning models, and how to deploy these models in production environments.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
To prepare for the Microsoft AI-102 certification exam, candidates should have a good understanding of AI concepts and experience working with Microsoft Azure. They should also be familiar with the Azure AI services and know how to use them to build AI solutions. Microsoft offers a variety of training resources, including self-paced online courses, classroom training, and hands-on labs, to help candidates prepare for the exam. Additionally, candidates can take practice exams and participate in study groups to gain a better understanding of the exam format and content.
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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Implement model monitoring and feedback |
| Topic 2: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Select suitable AI models - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |
| Topic 4: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Analyze images and detect objects/features - Process and index video content - Integrate vision capabilities into applications - Build and deploy custom vision models |
| Topic 5: Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Customize and deploy NLP models - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Topic 6: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service |
