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Exam Details
Speaking of the exam details, the test will contain from 40 to 60 questions of various types which you need to complete within 100 or 120 minutes (depends on the inclusion of labs). To pass the exam you need to schedule the test on the PearsonVUE platform, pay an exam fee which is currently $165, and score at least 700 points or more out of 1000. And, of course, the knowledge of exam topics is a must.
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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam
- Language: English, Japanese, Chinese (Simplified), Korean.
- Length of Examination: 50 mins
- This is beta exam.
- 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.)
- Type of Questions: This test format is multiple choice.
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 |
|---|---|---|
| Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Select suitable AI models - Monitor, optimize, and secure AI solutions |
| Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Analyze images and detect objects/features - Process and index video content - Extract text and handwriting from images - Build and deploy custom vision models |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization |
| Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types |
| Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Deploy and manage generative models - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |




