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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services
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Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
Obtaining a certification will make your resume more distinctive and help you have more opportunity in the future career. When you qualified with the Designing and Implementing a Microsoft Azure AI Solution certification, it means you have some special ability to deal with the case in the job. So, it seems that it is necessary to get the Designing and Implementing a Microsoft Azure AI Solution certification. When you are preparing for the actual test, please have a look at our Designing and Implementing a Microsoft Azure AI Solution pdf vce torrent.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models |
| Topic 2: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning |
| Topic 3: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service |
| Topic 4: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization |
| Topic 5: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Analyze images and detect objects/features - Process and index video content - Build and deploy custom vision models - Integrate vision capabilities into applications |
| Topic 6: 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 |
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