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Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:
| Passing Score | 700 / 1000 |
| Number of Questions | 40-60 |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Exam Price | $165 (USD) |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Exam Code | AI-102 |
| Duration | 130 mins |
| Schedule Exam | Pearson VUE |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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.
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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
| Topic 2: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Integrate Azure OpenAI and other generative models |
| Topic 3: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Process and index video content - Integrate vision capabilities into applications - Build and deploy custom vision models - Analyze images and detect objects/features |
| Topic 4: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources |
| Topic 5: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots |
| Topic 6: Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases |
