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Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.
Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate
For candidates that are aiming to develop their skills in building, operating, and deploying AI solutions with the help of such services as Azure Applied AI services and Azure Cognitive Services, the best variant is to pass the Microsoft AI-102 exam. This exam is all about designing and applying a Microsoft Azure AI Solution, and leads to getting the Microsoft Certified: Azure AI Engineer Associate certification.
Passing this exam implies that certified candidates are able to participate in all stages of AI solutions development from defining requirements to performance tuning and monitoring. These professionals cooperate with solution architects, as well as with data engineers and scientists, AI developers to show their vision and create comprehensive AI solutions.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
| Section | Objectives |
|---|---|
| Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Select appropriate Azure AI services - Manage security, compliance, and responsible AI |
| Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Implement custom vision models - Analyze images and videos using Azure AI Vision |
| Implement Generative AI Solutions | - Implement Azure OpenAI Service solutions - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques |
| Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Enrich and index data for search scenarios - Implement document intelligence solutions |
| Implement Natural Language Processing Solutions | - Analyze text using Azure AI Language - Implement question answering and conversational AI - Translate and interpret language using Azure AI services |
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