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Studiare a POLI.design significa accedere a un catalogo formativo per laureati e professionisti avanzato e altamente volto all'innovazione

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Mondadori Academy

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isqi.org

The ISTQB® Certified Tester AI Testing v2.0 (CT-AI) certification provides a comprehensive introduction to testing AI-based systems, with a primary focus on machine learning and including key concepts and testing approaches for generative AI. It extends core software testing knowledge to address the unique challenges of AI, including non-deterministic behavior, data dependency, probabilistic outcomes, and continuously evolving models. The certification equips professionals with the skills needed to design, execute, and evaluate tests for AI systems in modern development environments. Version 2.0 replaces the previous CT-AI v1.0 syllabus. The v1.0 certification will be phased out: English version: available until 21 April 2027 Non-English versions: available until 21 October 2027 Candidates are encouraged to pursue v2.0 to align with current industry practices and evolving AI technologies. Professionals interested in testing using generative AI systems should consider the ISTQB® Certified Tester – Testing with Generative AI (CT-GenAI) certification. Contents The ISTQB® CT-AI v2.0 certification covers the following key areas: Introduction to Artificial Intelligence AI-based vs conventional systems Types of AI (narrow, general, super AI) Overview of AI technologies, including generative AI AI development, hosting, and regulatory considerations Quality Characteristics for AI-Based Systems AI-specific quality characteristics (ISO/IEC 25059) Functional correctness and adaptability Transparency, robustness, and controllability Safety and ethical considerations Machine Learning Supervised, unsupervised, and reinforcement learning ML workflow and lifecycle Data preparation and dataset management ML functional performance metrics Neural networks fundamentals Testing AI-Based Systems Challenges in testing AI systems Statistical and risk-based testing approaches Testing generative AI and large language models Test levels for machine learning systems Input Data T

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