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To take the AI-102 certification exam, candidates should have a good understanding of Azure services, experience with data science and machine learning, and knowledge of programming languages such as Python and R. AI-102 exam consists of multiple-choice questions and performance-based tasks that evaluate the candidate's ability to design and implement AI solutions on Azure. Passing this certification exam demonstrates that the candidate has the skills and knowledge required to design and implement AI solutions using Microsoft Azure, which can help them advance their career in the field of AI and machine learning.
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Microsoft AI-102 (Designing and Implementing a Microsoft Azure AI Solution) Exam is a certification exam that focuses on testing the skills and knowledge of a candidate in designing and implementing AI solutions using Microsoft Azure. AI-102 exam is intended for individuals who have experience in working with Azure AI services and tools and want to obtain an official certification from Microsoft.
Microsoft AI-102 exam is a certification that validates a candidate's knowledge and skills in designing and implementing AI solutions on the Microsoft Azure platform. It is an advanced-level exam that requires a solid understanding of Azure services and tools, as well as expertise in AI technologies such as machine learning, natural language processing, and computer vision.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data |
| Topic 2: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots |
| Topic 3: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service |
| Topic 4: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Deploy and manage generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning |
| Topic 5: Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Select suitable AI models - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources |
| Topic 6: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Extract text and handwriting from images - Integrate vision capabilities into applications - Analyze images and detect objects/features - Process and index video content |
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