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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.
How to study the AI-102: Designing and Implementing an Azure AI Solution Exam
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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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
Microsoft AI-102 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage an Azure AI Solution | - Select appropriate Azure AI services - Plan solution requirements and architecture - Manage security, compliance, and responsible AI |
| Topic 2: Implement Knowledge Mining and Document Intelligence | - Implement document intelligence solutions - Build Azure AI Search solutions - Enrich and index data for search scenarios |
| Topic 3: Implement Computer Vision Solutions | - Implement custom vision models - Extract information using OCR and form recognition - Analyze images and videos using Azure AI Vision |
| Topic 4: Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Analyze text using Azure AI Language - Translate and interpret language using Azure AI services |
| Topic 5: Implement Generative AI Solutions | - Implement Azure OpenAI Service solutions - Integrate prompt engineering techniques - Build retrieval-augmented generation (RAG) solutions |
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