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Preparation Guide for AI-102: Designing and Implementing an Azure AI Solution Exam
Introduction
Microsoft has created a track for Azure professionals analyze the requirements for AI solutions, recommend appropriate tools and technologies, and implements solutions that meet scalability and performance requirements, to get certified this platform. solution architects translate the vision and work with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end solutions. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets Microsoft's proficiency standards.
This certification been actually designed for aspirants design and implement AI apps and agents that use Microsoft Azure Cognitive Services.
For this exam candidate having proficiency in using cognitive service APIs to meet business requirements, appropriate AI models and services, automation requirements, data privacy and protection , bot state data , cognitive service output would be an added advantage.
Certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work as AI solution architect and prove his knowledge, certification offered by Microsoft. This AI-102 Exam Certification helps a candidate to validates his skills in Azure platform.
In this guide, we will cover the AI-102: Designing and Implementing an Azure AI Solution Certification exam, AI-102: Designing and Implementing an Azure AI Solution Certified professional salary and all aspects of the AI-102: Designing and Implementing an Azure AI Solution Certification.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
| Section | Weight | Objectives |
|---|---|---|
| Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types |
| Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Process and index video content - Extract text and handwriting from images - Analyze images and detect objects/features - Build and deploy custom vision models |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization |
| 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 |
| Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Deploy and manage generative models |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select suitable AI models - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services |
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