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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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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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.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models |
| Topic 2: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents |
| Topic 3: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features - Extract text and handwriting from images |
| Topic 4: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval |
| Topic 5: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots |
| Topic 6: Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers |
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