AI Testing


The Certified Tester AI Testing (CT-AI) qualification is aimed at people who are seeking to extend their understanding of artificial intelligence and/or deep (machine) learning, most specifically testing AI based systems and using AI to test.

What are the entry criteria?

To achieve the CT-AI certification, candidates must hold the ISTQB® Certified Tester Foundation Level (CTFL) certificate.


The following image demonstrates the contents of the AI Testing syllabus which is part of the ISTQB® Specialist stream:

CT-AI Blocks

Business Outcomes

Individuals who hold the ISTQB® Certified Tester- AI Testing certification should be able to accomplish the following business outcomes:

  • Understand the current state and expected trends of AI
  • Experience the implementation and testing of a ML model and recognize where testers can best influence its quality
  • Understand the challenges associated with testing AI-Based systems, such as their self-learning capabilities, bias, ethics, complexity, non-determinism, transparency and explainability
  • Contribute to the test strategy for an AI-Based system
  • Design and execute test cases for AI-based systems
  • Recognize the special requirements for the test infrastructure to support the testing of AI-based systems
  • Understand how AI can be used to support software testing

Learning Objectives

Individuals who hold the ISTQB® Certified Tester- AI Testing certification should be able to to demonstrate their skills in the following areas:

  • Describe the AI effect and show how it influences the definition of AI
  • Distinguish between narrow AI, general AI, and super AI
  • Differentiate between AI-based systems and conventional systems
  • Recognize the different technologies used to implement AI
  • Identify popular AI development frameworks
  • Compare the choices available for hardware to implement AI-based systems
  • Explain the concept of AI as a Service (AIaaS)
  • Explain the use of pre-trained AI models and the risks associated with them
  • Describe how standards apply to AI-based systems
  • Explain the importance of flexibility and adaptability as characteristics of AI-based systems
  • Explain the relationship between autonomy and AI-based systems
  • Explain the importance of managing evolution for AI-based systems
  • Describe the different causes and types of bias for AI-based systems
  • Discuss the ethical principles that should be respected in the development, deployment and use of AI-based systems
  • Explain the occurrence of side effects and reward hacking in AI-based systems
  • Explain how transparency, interpretability and explainability apply to AI-based systems
  • Recall the characteristics that make it difficult to use AI-based systems in safety-related applications
  • Describe classification and regression as part of supervised learning
  • Describe clustering and association as part of unsupervised learning
  • Describe reinforcement learning
  • Summarize the workflow used to create an ML system
  • Given a project scenario, identify an appropriate ML approach (from classification, regression, clustering, association, or reinforcement learning)
  • Explain the factors involved in the selection of ML algorithms
  • Summarize the concepts of underfitting and overfitting
  • Demonstrate underfitting and overfitting
  • Describe the activities and challenges related to data preparation
  • Perform data preparation in support of the creation of an ML model
  • Contrast the use of training, validation and test datasets in the development of an ML model
  • Identify training and test datasets and create an ML model
  • Describe typical dataset quality issues
  • Recognize how poor data quality can cause problems with the resultant ML model
  • Recall the different approaches to the labelling of data in datasets for supervised learning
  • Recall reasons for the data in datasets being mislabeled
  • Calculate the ML functional performance metrics from a given set of confusion matrix data
  • Contrast and compare the concepts behind the ML functional performance metrics for classification, regression and clustering methods
  • Summarize the limitations of using ML functional performance metrics to determine the quality of the ML system
  • Select appropriate ML functional performance metrics and/or their values for a given ML model and scenario
  • Evaluate the created ML model using selected ML functional performance metrics
  • Explain the use of benchmark suites in the context of ML
  • Explain the structure and working of a neural network including a DNN
  • Experience the implementation of a perceptron
  • Describe the different coverage measures for neural networks
  • Explain how system specifications for AI-based systems can create challenges in testing
  • Describe how AI-based systems are tested at each test level
  • Recall those factors associated with test data that can make testing AI-based systems difficult
  • Explain automation bias and how this affects testing
  • Describe the documentation of an AI component and understand how documentation supports the testing of AI-based systems
  • Explain the need for frequently testing the trained model to handle concept drift
  • For a given scenario determine a test approach to be followed when developing an ML system
  • Explain the challenges in testing created by the self-learning of AI-based systems
  • Explain how autonomous AI-based systems are tested
  • Explain how to test for bias in an AI-based system
  • Explain the challenges in testing created by the probabilistic and non-deterministic nature of AI-based systems
  • Explain the challenges in testing created by the complexity of AI-based systems
  • Describe how the transparency, interpretability and explainability of AI-based systems can be tested
  • Use a tool to show how explainability can be used by testers
  • Explain the challenges in creating test oracles resulting from the specific characteristics of AI-based systems
  • Select appropriate test objectives and acceptance criteria for the AI-specific quality characteristics of a given AI-based system
  • Explain how the testing of ML systems can help prevent adversarial attacks and data poisoning
  • Explain how pairwise testing is used for AI-based systems
  • Apply pairwise testing to derive and execute test cases for an AI-based system
  • Explain how back-to-back testing is used for AI-based systems
  • Explain how A/B testing is applied to the testing of AI-based systems
  • Apply metamorphic testing for the testing of AI-based systems
  • Apply metamorphic testing to derive test cases for a given scenario and execute them
  • Explain how experience-based testing can be applied to the testing of AI-based systems
  • Apply exploratory testing to an AI-based system
  • For a given scenario select appropriate test techniques when testing an AI-based system
  • Describe the main factors that differentiate the test environments for AI-based systems from those required for conventional systems
  • Describe the benefits provided by virtual test environments in the testing of AI-based systems
  • Categorize the AI technologies used in software testing
  • Discuss, using examples, those activities in testing where AI is less likely to be used
  • Explain how AI can assist in supporting the analysis of new defects
  • Explain how AI can assist in test case generation
  • Explain how AI can assist in optimization of regression test suites
  • Explain how AI can assist in defect prediction
  • Implement a simple AI-based defect prediction system
  • Explain the use of AI in testing user interfaces


The ISTQB® CT-AI certification exam is based on the Certified Tester AI Testing (CT-AI) syllabus.

The International Software Testing Qualifications Board (ISTQB®) provides the syllabus to its Member Boards for them to accredit training providers. The syllabus is also used to derive examination questions (including in local language where available).

Training providers will produce courseware and determine appropriate teaching methods for accreditation using the syllabus. The syllabus will help candidates in their preparation for the examination.

The full package of documents is available in Materials for download section.

Exam Structure

The specialist stream AI Testing exam consists of 40 multiple-choice questions, with a pass mark grade of 65% to be completed within 60 minutes. Participants that take the exam not in their spoken language, will receive additional 25% time, and will have 15 minutes more, or a total of 75 min.

Module Number of questions Exam length (minutes) Exam length +25% (minutes)
AI Testing 40 60 75

Accredited training providers

Exams may be taken as part of a course delivered by an Accredited Training Provider or taken independently at an examination center or in a public exam.

The typical duration of the AI Testing Certification Training offered by an Accredited Training Provider is 4 days. Completion of an accredited training course is not a prerequisite for participating in the exam.


Materials for Download

Syllabus Documents:

pdf.pngISTQB CT-AI Syllabus v1.0Size 1.63 MB


ebook.pngISTQB® Certified Tester AI Testing SyllabusSize 1.2 MB


Exam Documents:

pdf.pngISTQB CT-AI Sample Exam Questions v1.0Size 433.03 KB


pdf.pngISTQB CT-AI Sample Exam Answers v1.0Size 500.53 KB


pdf.pngISTQB Exam Structures and Rules 


pdf.pngISTQB Exam Structure Tables 


ebook.pngISTQB® Certified Tester AI Testing Sample Exam QuestionsSize 277.56 KB


ebook.pngISTQB® Certified Tester AI Testing Sample Exam AnswersSize 281.67 KB


General Files:

pdf.pngISTQB CT-AI Release Notes v1.0Size 86.58 KB


pdf.pngISTQB CT-AI Overview v1.0Size 513.56 KB


pdf.pngISTQB CT-AI Accreditation Guidelines v1.0Size 170.41 KB