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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.
Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Designing and Implementing a Microsoft Azure AI Solution Exam Certification Details:
| Schedule Exam | Pearson VUE |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Passing Score | 700 / 1000 |
| Duration | 130 mins |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Exam Price | $165 (USD) |
| Exam Code | AI-102 |
| Number of Questions | 40-60 |
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data |
| Topic 2: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Select suitable AI models |
| Topic 3: Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models |
| Topic 4: 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 |
| Topic 5: Implement computer vision solutions | 10-15% | - Extract text and handwriting from images - Integrate vision capabilities into applications - Build and deploy custom vision models - Analyze images and detect objects/features - Process and index video content |
| Topic 6: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models |
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