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CT-AI — AI Testing

ISTQB · Topic 14 of 17

Test Environments for AI-Based Systems

Reproducibility constraints, GPU/TPU farms, simulation environments, hardware-in-the-loop for AI.

6 lessons240 questions
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What this topic covers

  1. 01Test Environments for AI-Based Systems: Quality, Metrics, and Fairness480m
  2. 02Follow-up Test Cases and Input Data Testing for AI Systems480m
  3. 03Macro-averaging and Micro-averaging for Classification Metrics360m
  4. 04Oracle Problem, Output Quality, and Key Metrics for AI Testing480m
  5. 05Source Test Cases and Metamorphic Testing for AI Systems480m
  6. 06Regression Metrics: RMSE, Adjusted R-squared, and Coefficient of Determination360m

Other topics in CT-AI — AI Testing

Introduction to Artificial Intelligence7 lessonsIntroduction to AI6 lessonsMachine Learning Overview6 lessonsInput Data Testing6 lessonsML Data6 lessonsMachine Learning Model Testing7 lessonsML Functional Performance Metrics6 lessonsMachine Learning Functional Performance Metrics6 lessonsTesting Generative AI Systems6 lessonsQuality Characteristics for AI-Based Systems4 lessonsTesting AI-Specific Quality Characteristics6 lessonsML Neural Networks and Testing6 lessonsTesting AI-Based Systems Overview6 lessonsMachine Learning Development Testing3 lessonsUsing AI for Testing6 lessonsMethods and Techniques for Testing AI6 lessons