ISTQB® Certified Tester AI Testing (CT-AI)

Boost your capability to evaluate AI-based systems by strengthening your understanding of artificial intelligence and deep learning, with a clear focus on testing AI systems and leveraging AI within your testing processes.

The ISTQB® Certified Tester – AI Testing (CT-AI) course builds practical capability in testing AI-based systems and applying AI to strengthen your testing approach. Aligned to the ISTQB CT-AI v2.0 syllabus, it gives you a clear view of today’s AI landscape and emerging trends—so you can plan, design and execute effective tests for machine learning and generative AI solutions.

By the end of the course, you will be able to apply AI to support and improve software testing activities, test AI-based systems (including machine learning and generative AI) while accounting for probabilistic behaviour, non-determinism and data dependency, design meaningful risk-based test cases for AI solutions, and contribute confidently to an AI-focused test strategy and quality engineering approach.

Course objectives:

• Explain today’s AI landscape (ML and Generative AI) and where it creates business value.
• Distinguish AI-driven products from traditional software to set realistic quality expectations.
• Interpret how models learn (including neural networks) to target testing where it matters most.
• Assess AI quality risks: transparency, explainability, fairness, robustness, safety and trustworthiness.
• Define clear AI acceptance criteria and use them to judge real system behaviour.
• Validate training, test and live data to reduce defects, bias and performance volatility.
• Detect and respond to bias, data drift and data quality issues before they impact customers.
• Calculate and interpret key ML performance metrics to evidence release readiness.
• Design multi-level tests for ML systems to improve reliability from component to production.
• Evaluate model resilience under edge cases, changing inputs and adversarial conditions.
• Apply fit-for-purpose testing across data prep, training, validation, deployment and monitoring.
• Manage probabilistic and non-deterministic behaviour with practical risk mitigations.
• Test Generative AI and LLM outputs for accuracy, consistency, safety and policy compliance.
• Identify common GenAI failure modes (hallucination, prompt injection, data leakage) and test for them.
• Contribute to an AI test strategy and select techniques that increase confidence and reduce delivery risk.

Target Audience:

• Anyone involved in testing AI-based systems and/or AI for testing.
• Testers, test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers.
• Anyone who wants a basic understanding of testing AI-based systems and/or AI for testing.
• Project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants working with an AI-based system.
• Data scientists, ML engineers and professionals contributing to AI-enabled product development.

Prerequisites:

The candidate must hold the ISTQB Foundation certificate to undertake the ISTQB AI Tester course. A minimum of 12 months’ previous testing experience is also recommended. This experience is beneficial but not required by the ISTQB.

 

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