ISTQB Certified Tester AI Testing (CT-AI) Preparation

Explore key quality characteristics for AI-based systems, including flexibility, adaptability, autonomy, bias mitigation, ethical considerations, transparency, interpretability, accountability, fairness, and safety, to strengthen user trust and reliable performance.

Explore machine learning from data to improve performance, covering supervised, unsupervised, and reinforcement learning, key workflows, model selection, evaluation, and real-world applications in healthcare, finance, marketing, transportation, and entertainment.

Navigate data preparation for machine learning by acquiring, cleaning, and transforming data; engineer features, perform EDA, and ensure quality, labeling, and proper dataset splits.

Explore key ML performance metrics: accuracy, precision, recall, and F1 score, through confusion matrix visuals, ROC curves, AUC, and benchmarks for classification, regression, and clustering.

Explore neural networks, their layered architecture and training, and apply coverage measures—neuron, layer, and structural, plus adversarial testing to boost reliability in image and speech tasks.

Explore testing ai based systems to ensure reliability, robustness, and effectiveness, addressing non-determinism, data quality, bias, and concept drift with unit, integration, and system testing.

Explore AI-specific quality characteristics testing for self-learning and autonomous systems, covering reliability, bias management, transparency, and interpretability through robust testing frameworks, simulation, and ongoing monitoring.

Explore testing methods for AI-based systems, focusing on performance, security, and reliability. Apply adversarial, pairwise, AB, metamorphic, data poisoning, and exploratory testing to strengthen model resilience.

Examine test environments for AI systems, simulating real-world conditions to evaluate performance and functionality, using virtual environments to manage data quality, learning capability, and GPUs or TPUs.