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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
How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Select suitable AI models - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles |
| Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models |
| Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Build knowledge bases and search indexes |
| 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 |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Understand agent use cases and types |
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