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Microsoft AI-102 exam is a challenging but rewarding certification that can help professionals demonstrate their expertise in designing and implementing AI solutions on the Azure platform. Whether you're looking to advance your career, improve your skills, or stay up-to-date with the latest technologies, AI-102J exam is an excellent way to showcase your capabilities and stand out in the competitive AI job market.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft AI-102 exam measures the candidate's ability to design and implement AI solutions that meet business requirements. It covers a wide range of topics, such as data preprocessing, feature engineering, model training, model deployment, and monitoring. Candidates should have a good understanding of machine learning algorithms, programming languages such as Python and R, and Azure services such as Azure Storage, Azure Kubernetes Service, and Azure Functions.
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents |
| Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models - Select appropriate Microsoft Foundry Services |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Deploy and manage generative models - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Implement computer vision solutions | 10-15% | - Process and index video content - Build and deploy custom vision models - Integrate vision capabilities into applications - Analyze images and detect objects/features - Extract text and handwriting from images |
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