AI-900, AI-100: Microsoft Azure IA

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AI-900: Principes fondamentaux de l'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; toutefois, 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, mais ce n'est une condition préalable pour aucun d'entre eux.

AI-100: Designing and Implementing an Azure AI Solution

Candidates for this exam should have subject matter expertise using cognitive services, apprentissage automatique, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, discours, vision par ordinateur, 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, ingénieurs de données, Spécialistes IdO, 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, Service de bot Azure, Recherche cognitive Azure, et stockage de données dans Azure. en outre, 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.