Last Updated: Aug 31, 2026
No. of Questions: 425 Questions & Answers with Testing Engine
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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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Languages: English, Japanese, Chinese (Simplified), Korean
Retirement date: none
This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions; and implement conversational AI solutions.
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The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select suitable AI models - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Topic 2: 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 |
| Topic 3: 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 4: Implement computer vision solutions | 10-15% | - Process and index video content - Analyze images and detect objects/features - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models |
| Topic 5: 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 |
| Topic 6: 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 |
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