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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: Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles |
| Topic 2: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases |
| Topic 3: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback |
| Topic 4: Implement computer vision solutions | 10-15% | - Process and index video content - Build and deploy custom vision models - Analyze images and detect objects/features - Extract text and handwriting from images - Integrate vision capabilities into applications |
| Topic 5: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization - Build conversational AI and chatbots - Customize and deploy NLP models |
| Topic 6: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents |
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