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This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions; and implement conversational AI solutions.
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AI-102: Designing and Implementing an Azure AI Solution Certification Path
The Microsoft Designing and Implementing an Azure AI Solution Certification includes only one AI-100 Exam.
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AI-102: Designing and Implementing an Azure AI Solution Exam Certified Professional salary
The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in
- United State - 120,000 USD
- Europe - 90,547 EURO
- England - 90,532 POUND
- India - 20,00,327 INR
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services |
| Topic 2: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Build and deploy custom vision models - Extract text and handwriting from images - Integrate vision capabilities into applications |
| Topic 3: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Implement model monitoring and feedback |
| Topic 4: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Develop multi-agent workflows and orchestration |
| Topic 5: Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Ingest and process structured/unstructured data |
| Topic 6: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization |
