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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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Who should take the AI-102: Designing and Implementing an Azure AI Solution Exam

The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.

For more info read reference:

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

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

Microsoft AI-102日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement natural language processing solutions15-20%- Perform text analysis, sentiment detection, and language detection
- Implement translation and summarization
- Customize and deploy NLP models
- Build conversational AI and chatbots
Topic 2: Implement computer vision solutions10-15%- Extract text and handwriting from images
- Process and index video content
- Build and deploy custom vision models
- Integrate vision capabilities into applications
- Analyze images and detect objects/features
Topic 3: Plan and manage an Azure AI solution20-25%- Create and configure Azure AI resources
- Select appropriate Microsoft Foundry Services
- Select suitable AI models
- Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining
- Plan solutions aligned with responsible AI principles
- Monitor, optimize, and secure AI solutions
Topic 4: Implement generative AI solutions15-20%- Integrate Azure OpenAI and other generative models
- Apply prompt engineering and fine-tuning
- Implement model monitoring and feedback
- Deploy and manage generative models
- Orchestrate multiple models and containers
Topic 5: Implement an agentic solution5-10%- Test, deploy, and optimize agents
- Develop multi-agent workflows and orchestration
- Understand agent use cases and types
- Build agents with Microsoft Foundry Agent Service
Topic 6: Implement knowledge mining and information extraction solutions15-20%- Ingest and process structured/unstructured data
- Build knowledge bases and search indexes
- Extract entities, relationships, and key phrases
- Implement intelligent search and retrieval

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