CT-AI

The ISTQB Certified Tester - Artificial Intelligence (CT-AI) exam focuses on the application of testing principles and techniques in the context of AI systems. Here's a breakdown of its main points:

Understanding AI Systems

• Definition of AI and key AI concepts: Machine learning, deep learning, natural language processing, computer vision, etc.
• Different types of AI systems: Supervised, unsupervised, reinforcement learning.
• Common AI system architectures: Neural networks, decision trees, Bayesian networks.
• AI system lifecycle, including development and maintenance.

Testing AI Systems

• Specific challenges in testing AI systems:
  • Black box nature of AI models.
  • Difficulty in understanding model decisions.
  • Data-driven nature of AI systems.
  • Evolving nature of AI systems.
• Testing techniques for AI systems:
  • Unit testing of AI components.
  • Integration testing of AI system components.
  • Functional testing of AI system behavior.
  • Non-functional testing of AI system performance, security, and usability.
  • Regression testing of changes in AI models.
  • Model explainability and interpretability testing.
• Data-driven techniques for AI testing:
  • Data quality assessment and testing.
  • Data bias detection and testing.
  • Data drift analysis and testing.
  • Data augmentation techniques.

AI Testing Tools and Frameworks

• Overview of popular testing tools and frameworks for AI systems:
  • Automated testing frameworks.
  • Model explainability tools.
  • Data analysis and visualization tools.
  • Performance and stress testing tools.
• Choosing the right tools and frameworks for specific AI testing tasks.

Ethical and Societal Considerations

• Ethical implications of AI systems:
  • Bias and fairness in AI models.
  • Privacy and security concerns.
  • Responsible use of AI.
• Testing for ethical considerations:
  • Bias detection and mitigation.
  • Privacy impact assessments.
  • Safety and security testing.

Beyond the Basics

• Advanced topics in AI testing:
  • Testing for adversarial attacks.
  • Testing for the robustness of AI systems.
  • Testing for explainability and interpretability.
  • Testing for AI system reliability and maintainability.
• Future trends in AI testing.

Exam Format

• Multiple-choice questions covering various topics related to AI testing.
• Sample questions can be found in ISTQB resources online.

Preparation

• Study official ISTQB CT-AI syllabus and materials.
• Familiarize yourself with AI testing tools and frameworks.
• Practice with sample questions and practice exams.

This information provides a general overview of the main points discussed in the ISTQB CT-AI Exam. Remember to consult the official ISTQB resources for the most up-to-date and comprehensive information.