AI-102: Designing and Implementing an Azure AI Solution Certification Path
The Microsoft Designing and Implementing an Azure AI Solution Certification includes only one AI-100 Exam.
Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.
Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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 - Implement translation and summarization - Customize and deploy NLP models - Build conversational AI and chatbots |
| Topic 2: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - 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 - Select appropriate Microsoft Foundry Services |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases |
| Topic 4: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning |
| Topic 5: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration |
| Topic 6: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Analyze images and detect objects/features - Extract text and handwriting from images - Integrate vision capabilities into applications - Process and index video content |
