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The Microsoft AI-102 exam covers various topics related to AI solution development, including designing and implementing AI workflows, creating natural language processing solutions, leveraging computer vision techniques, and implementing machine learning models. It also tests the candidate's ability to work with Azure services such as Azure Cognitive Services, Azure Machine Learning, and Azure Databricks.
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Microsoft AI-102 exam is a certification that validates the skills and knowledge of professionals in designing and implementing AI solutions using Microsoft Azure. AI-102 exam is intended for developers, data scientists, and AI professionals who want to demonstrate their expertise in designing and developing AI solutions on the Azure platform. Candidates who pass AI-102 exam will earn the Microsoft Certified: Azure AI Engineer Associate certification.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Topic 2: Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Test, deploy, and optimize agents |
| Topic 3: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models |
| Topic 4: 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 |
| Topic 5: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Build and deploy custom vision models - Analyze images and detect objects/features - Process and index video content - Integrate vision capabilities into applications |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Select suitable AI models - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles |
