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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
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Candidates for AI-102 Exam are seeking to prove fundamental knowledge and skills in Designing and Implementing an Azure AI Solution domain. Before taking this exam, aspirants ought to have a solid fundamental information of the concepts shared in preparation guide as well as basic understanding of Azure administration, Azure development, and DevOpss would give an added edge.
This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio.
It is suggested that professionals accustomed to the ideas and also the technologies represented here by taking relevant training courses. Candidates are expected to have some hands-on experience on bot services that use Language Understanding , bots with Azure Application Insights, creating a GPU, FPGA, or CPU-based solution, implementing AI workflow.
After passing this exam, candidates get a certificate from Microsoft that helps them to demonstrate their proficiency to their clients and employers.
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| Duration | 130 mins |
| Exam Code | AI-102 |
| Exam Name | Microsoft Certified - Azure AI Engineer Associate |
| Exam Price | $165 (USD) |
| Number of Questions | 40-60 |
| Sample Questions | Designing and Implementing a Microsoft Azure AI Solution Sample Questions |
| Books / Training | Course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution |
| Schedule Exam | Pearson VUE |
| Passing Score | 700 / 1000 |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service |
| Topic 2: Implement computer vision solutions | 10-15% | - Process and index video content - Integrate vision capabilities into applications - Build and deploy custom vision models - Extract text and handwriting from images - Analyze images and detect objects/features |
| Topic 3: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select suitable AI models - Select appropriate Microsoft Foundry Services |
| Topic 4: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Implement intelligent search and retrieval |
| Topic 5: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models |
| Topic 6: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models |
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