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How to study the AI-102: Designing and Implementing an Azure AI Solution Exam
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization |
| Topic 2: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Analyze images and detect objects/features - Extract text and handwriting from images - Process and index video content - Integrate vision capabilities into applications |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Build knowledge bases and search indexes |
| Topic 4: 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 5: Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Orchestrate multiple models and containers - Implement model monitoring and feedback |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select suitable AI models |
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