- Exam Code: AI-102
- Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)
- Certification Provider: Microsoft
- Corresponding Certification:Azure AI Engineer Associate
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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
- Europe - 90,547 EURO
- India - 20,00,327 INR
- United State - 120,000 USD
- England - 90,532 POUND
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For candidates that are aiming to develop their skills in building, operating, and deploying AI solutions with the help of such services as Azure Applied AI services and Azure Cognitive Services, the best variant is to pass the Microsoft AI-102 exam. This exam is all about designing and applying a Microsoft Azure AI Solution, and leads to getting the Microsoft Certified: Azure AI Engineer Associate certification.
Passing this exam implies that certified candidates are able to participate in all stages of AI solutions development from defining requirements to performance tuning and monitoring. These professionals cooperate with solution architects, as well as with data engineers and scientists, AI developers to show their vision and create comprehensive AI solutions.
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Skills measured
- Implement knowledge mining solutions (15-20%)
- Plan and manage an Azure Cognitive Services solution (15-20%)
- Implement natural language processing solutions (20-25%)
- Implement conversational AI solutions (15-20%)
- Implement Computer Vision solutions (20-25%)
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Extract text and handwriting from images - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features |
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Test, deploy, and optimize agents |
| Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources |
| Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Implement model monitoring and feedback - Deploy and manage generative models |
| 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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