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How much AI-102: Designing and Implementing an Azure AI Solution Exam Cost
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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How to Prepare For AI-102: Designing and Implementing an Azure AI Solution Exam
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.
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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Extract text and handwriting from images - Build and deploy custom vision models - Integrate vision capabilities into applications - Process and index video content |
| Implement natural language processing solutions | 15-20% | - Build conversational AI and chatbots - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization |
| 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 |
| Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Select suitable AI models - Plan solutions aligned with responsible AI principles - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources |
| Implement knowledge mining and information extraction solutions | 15-20% | - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |
| Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Implement model monitoring and feedback |





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