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Skills measured
- Implement natural language processing solutions (20-25%)
- Implement conversational AI solutions (15-20%)
- Implement knowledge mining solutions (15-20%)
- Implement Computer Vision solutions (20-25%)
- Plan and manage an Azure Cognitive Services solution (15-20%)
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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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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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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service |
| 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 - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval |
| Topic 4: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Process and index video content - Extract text and handwriting from images - Build and deploy custom vision models - Analyze images and detect objects/features |
| Topic 5: Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Select suitable AI models - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles |
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