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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 2	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 3	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 4	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 5	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 6	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 7	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 8	systems from those required for conventional systems.
Topic 9	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
Which ONE of the following statements about a system MOST describes an autonomous system?

A. A fraud detection system that alerts operators when it sees a specific fraud risk associated with a transaction, in order to obtain human input to make a final decision.
B. A self-driving car that automatically stops the car if the driver is not responding to voice prompts, in order to ensure they are awake.
C. A loan approval system that can continue to process loan applications indefinitely, within a defined amount of aggregate credit, until an operator reduces the amount of aggregate credit available.
D. A chatbot that learns the most effective responses to humans (in order to ensure the humans continue to converse) based on prior experiences.

Answer: B

Explanation:
An autonomous system is capable of performing tasks independently, without requiring human intervention. The self-driving car in option B is an example of an autonomous system because it automatically takes action (stopping the car) based on its internal decision-making, in response to the driver's behavior.

NEW QUESTION # 11
The following confusion matrix represents the functional performance of a classifier.

Which ONE of the following is the correct calculation for the accuracy of the classifier?

A. 60 / (60 + 9) * 100% = 87%
B. (60 + 11) / (60 + 11 + 20 + 9) * 100% = 71%
C. 60 / (60 + 20) * 100% = 75%
D. 2 * (60 / (60 + 20) * 60 / (60 + 9)) / (60 / (60 + 20) + 60 / (60 + 9)) * 100% = 80%

Answer: B

Explanation:
Accuracy = (True Positives + True Negatives) / (Total Samples)
In this case:
- True Positives (TP) = 60
- True Negatives (TN) = 11
- False Positives (FP) = 20
- False Negatives (FN) = 9
Accuracy = (60 + 11) / (60 + 11 + 20 + 9) * 100% = 71%

NEW QUESTION # 12
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)

A. In classification, objects are always assigned to exactly two classes
B. Deciding whether an object is a bicycle or a motorcycle is a classification problem
C. Recognizing a dog from many different images is a regression problem
D. Predicting that diesel prices will increase by ~10% is a classification problem

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches.
It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values.
OptionB-deciding whether an object is a bicycle or a motorcycle-fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregressionproblem. Option D is incorrect because classification can involveany number of classes, not only two. Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.

NEW QUESTION # 13
Which statement about the property of the test environment for an AI-based system is correct?

A. The test environment for an AI-based multi-agent system needs to act deterministically.
B. The test environment for a self-learning AI system needs to adapt to and learn from the test object.
C. The test environment for an autonomous AI system needs to perform both test design and execution autonomously.
D. The test environment for an AI system may need to include tools that can explain the decisions of the test object.

Answer: D

Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may require specialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making Option B correct.

NEW QUESTION # 14
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI based system?

A. Unexpected changes in system behaviour can occur
B. Obtaining test data is harder
C. It is necessary to measure how changes in the test inputs cause changes in the test outputs
D. Sometimes the system can only be tested as a black-box

Answer: D

Explanation:
The correct answer is D . The syllabus explains that AI-based systems are often used for tasks too complex for humans to perform, and this can create a test oracle problem because testers may not be able to determine expected results in the usual way. It also states that when the internal structure of an AI-based system is generated by software, the structure may be too complex for humans to understand, leading to the situation where the system can only be tested as a black box.
Option A concerns test data acquisition, which is a general AI testing challenge but not the central complexity mechanism identified in this question. Option B describes an approach used in explainability or bias-related investigation, especially model-agnostic reasoning about input-output sensitivity. Option C is more characteristic of self-learning systems, where the system may change its own behaviour over time.
Complexity primarily causes difficulty because expected behaviour, internal logic, component interactions, and test oracles become hard to understand or define.
References/topics: CT-AI Syllabus Chapter 8, Section 8.5 "Challenges Testing Complex AI-Based Systems"; Section 8.7 "Test Oracles for AI-Based Systems."
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NEW QUESTION # 15
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