Skills measured
- Implement natural language processing solutions (20-25%)
- Implement Computer Vision solutions (20-25%)
- Plan and manage an Azure Cognitive Services solution (15-20%)
- Implement conversational AI solutions (15-20%)
- Implement knowledge mining solutions (15-20%)
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Introduction to AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
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Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data |
| Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features - Extract text and handwriting from images |
| Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents |
| Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models - Implement model monitoring and feedback |
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources |
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models |
