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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
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Exam Code | AI-102 |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Number of Questions | 40-60 |
| Passing Score | 700 / 1000 |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Schedule Exam | Pearson VUE |
| Exam Price | $165 (USD) |
| Duration | 130 mins |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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| Section | Objectives |
|---|---|
| Topic 1: Implement Knowledge Mining and Document Intelligence | - Implement document intelligence solutions - Enrich and index data for search scenarios - Build Azure AI Search solutions |
| Topic 2: Implement Generative AI Solutions | - Implement Azure OpenAI Service solutions - Integrate prompt engineering techniques - Build retrieval-augmented generation (RAG) solutions |
| Topic 3: Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Manage security, compliance, and responsible AI - Select appropriate Azure AI services |
| Topic 4: 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 |
| Topic 5: Implement Computer Vision Solutions | - Analyze images and videos using Azure AI Vision - Implement custom vision models - Extract information using OCR and form recognition |
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