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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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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.
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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.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Design document processing pipelines - Implement Azure AI Search solutions - Create custom skills for Azure AI Search - Implement semantic and vector search |
| Topic 2: Implement Natural Language Processing Solutions | 20-25% | - Analyze text using Azure AI Language - Create conversational AI solutions with Azure AI Bot Service - Translate text and speech - Build question answering solutions - Implement speech capabilities using Azure AI Speech |
| Topic 3: Implement Computer Vision Solutions | 15-20% | - Implement Azure AI Face service - Implement object detection and image classification - Process video and generate insights - Analyze images using Azure AI Vision - Read and process documents using Azure AI Document Intelligence |
| Topic 4: Implement Generative AI Solutions | 10-15% | - Integrate Azure OpenAI models into applications - Use Azure OpenAI Service for generative AI - Apply responsible AI practices - Implement prompt engineering techniques |
| Topic 5: Implement Decision Support Solutions | 10-15% | - Integrate Azure AI services into applications - Monitor and optimize decision support solutions - Design and implement decision automation |
| Topic 6: Plan and Manage an Azure AI Solution | 15-20% | - Select appropriate Azure AI services and technologies - Plan and configure secure AI solutions - Manage AI costs and resource allocation - Implement governance and compliance for AI solutions |
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