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This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions; and implement conversational AI solutions.
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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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types |
| Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Build and deploy custom vision models - Analyze images and detect objects/features - Integrate vision capabilities into applications - Process and index video content |
| Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Ingest and process structured/unstructured data |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers |
| 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 |
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