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AI-900, AI-100: Microsoft Azure AI

AI-900, AI-100: Microsoft Azure AI

Precio: $19.99

AI-900: Fundamentos de la IA de Microsoft Azure

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; sin embargo, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, pero no es un requisito previo para ninguno de ellos.

AI-100: Designing and Implementing an Azure AI Solution

Candidates for this exam should have subject matter expertise using cognitive services, aprendizaje automático, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, and conversational AI.

Responsibilities for an Azure AI Engineer include analyzing requirements for AI solutions, recommending the appropriate tools and technologies, and designing and implementing AI solutions that meet scalability and performance requirements.

Azure AI Engineers translate the vision from solution architects and work with data scientists, ingenieros de datos, Especialistas en IoT, and software developers to build complete end-to-end solutions.

A candidate for this exam should have knowledge and experience designing and implementing AI apps and agents that use Microsoft Azure Cognitive Services, Servicio Azure Bot, Búsqueda cognitiva de Azure, y almacenamiento de datos en Azure. Adicionalmente, a candidate should be able to recommend solutions that use open source technologies, understand the components that make up the Azure AI portfolio and the available data storage options, and understand when a custom API should be developed to meet specific requirements.

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