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The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection |
| Topic 2: Implement computer vision solutions | 10-15% | - Process and index video content - Build and deploy custom vision models - Extract text and handwriting from images - Analyze images and detect objects/features - Integrate vision capabilities into applications |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval |
| Topic 4: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Select suitable AI models - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Topic 5: Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents |
| Topic 6: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models |
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