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.
Prerequisites
Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam
- Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)
- This is beta exam.
- Type of Questions: This test format is multiple choice.
- Length of Examination: 50 mins
- Passing Score: 700 / 1000
- Language: English, Japanese, Chinese (Simplified), Korean.
How to Register For Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution?
Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models |
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Understand agent use cases and types |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Build knowledge bases and search indexes |
