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These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
For candidates that are aiming to develop their skills in building, operating, and deploying AI solutions with the help of such services as Azure Applied AI services and Azure Cognitive Services, the best variant is to pass the Microsoft AI-102 exam. This exam is all about designing and applying a Microsoft Azure AI Solution, and leads to getting the Microsoft Certified: Azure AI Engineer Associate certification.
Passing this exam implies that certified candidates are able to participate in all stages of AI solutions development from defining requirements to performance tuning and monitoring. These professionals cooperate with solution architects, as well as with data engineers and scientists, AI developers to show their vision and create comprehensive AI solutions.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Topic 2: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Process and index video content - Build and deploy custom vision models - Integrate vision capabilities into applications - Analyze images and detect objects/features |
| Topic 3: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Select suitable AI models - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services |
| Topic 4: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Implement model monitoring and feedback |
| Topic 5: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models - Implement translation and summarization |
| Topic 6: 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 |
Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
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Designing and Implementing a Microsoft Azure AI Solution
Designing and Implementing an Azure AI Solution
Developing AI Apps and Agents on Azure (AI-103日本語版)
Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
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