What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam
- Language: English, Japanese, Chinese (Simplified), Korean.
- This is beta exam.
- Type of Questions: This test format is multiple choice.
- Length of Examination: 50 mins
- Passing Score: 700 / 1000
- Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)
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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
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Skills measured
- Plan and manage an Azure Cognitive Services solution (15-20%)
- Implement knowledge mining solutions (15-20%)
- Implement conversational AI solutions (15-20%)
- Implement natural language processing solutions (20-25%)
- Implement Computer Vision solutions (20-25%)
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Select suitable AI models - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services |
| Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration |
| 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 natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Extract text and handwriting from images - Build and deploy custom vision models - Process and index video content - Integrate vision capabilities into applications |
