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.
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For more info read reference:
microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services
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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.
AI-102: Designing and Implementing an Azure AI Solution Exam Certified Professional salary
The average salary of a AI-102: Designing and Implementing an Azure AI Solution Exam Certified Expert in
- United State - 120,000 USD
- Europe - 90,547 EURO
- England - 90,532 POUND
- India - 20,00,327 INR
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 |
|---|---|---|
| Topic 1: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Implement translation and summarization - Customize and deploy NLP models - Build conversational AI and chatbots |
| Topic 2: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Deploy and manage generative models |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases |
| Topic 4: Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select suitable AI models - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Topic 5: 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 |
| Topic 6: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Extract text and handwriting from images - Process and index video content - Build and deploy custom vision models - Analyze images and detect objects/features |





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