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How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=AI-102&examname=Exam%20AI-102:%20Designing%20and%20Implementing%20a%20Microsoft%20Azure%20AI%20Solution&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fai-102

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution

Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.

They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.

Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.

They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate

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Microsoft AI-102 Korean Exam Syllabus Topics:

SectionWeightObjectives
Implement natural language processing solutions15-20%- Perform text analysis, sentiment detection, and language detection
- Customize and deploy NLP models
- Implement translation and summarization
- Build conversational AI and chatbots
Implement computer vision solutions10-15%- Process and index video content
- Build and deploy custom vision models
- Integrate vision capabilities into applications
- Extract text and handwriting from images
- Analyze images and detect objects/features
Plan and manage an Azure AI solution20-25%- Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining
- Plan solutions aligned with responsible AI principles
- Select suitable AI models
- Select appropriate Microsoft Foundry Services
- Monitor, optimize, and secure AI solutions
- Create and configure Azure AI resources
Implement generative AI solutions15-20%- Integrate Azure OpenAI and other generative models
- Apply prompt engineering and fine-tuning
- Deploy and manage generative models
- Implement model monitoring and feedback
- Orchestrate multiple models and containers
Implement an agentic solution5-10%- Build agents with Microsoft Foundry Agent Service
- Test, deploy, and optimize agents
- Understand agent use cases and types
- Develop multi-agent workflows and orchestration
Implement knowledge mining and information extraction solutions15-20%- Ingest and process structured/unstructured data
- Extract entities, relationships, and key phrases
- Build knowledge bases and search indexes
- Implement intelligent search and retrieval

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