How much AI-102: Designing and Implementing an Azure AI Solution Exam Cost
The price of the Microsoft Mobility and Devices Fundamentals exam is $165 USD, for more information related to exam price please visit to Microsoft Training website as prices of Microsoft exams fees get varied country wise.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
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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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Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Select suitable AI models |
| Topic 2: Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service |
| Topic 3: 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 |
| Topic 4: Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases |
| Topic 5: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Extract text and handwriting from images - Build and deploy custom vision models - Integrate vision capabilities into applications |
| Topic 6: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Integrate Azure OpenAI and other generative models |






