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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • systems from those required for conventional systems.
Topic 2
  • 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 3
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4
  • 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 5
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 7
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q113-Q118):

NEW QUESTION # 113
Which of the following statements about bias in AI-based systems is MOST correct?

Answer: C

Explanation:
Inappropriate bias in AI systems is typically caused by the data used for training not being representative of the real world. This can lead to the model making biased or incorrect predictions when applied to real-world scenarios. While algorithmic factors can also contribute to bias, the primary issue arises from biased or unrepresentative training data.


NEW QUESTION # 114
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

Answer: B

Explanation:
The correct answer is C . The CT-AI syllabus states that ML models trained on the features of the most critical defects can be used to identify defects most likely to cause system failures that account for a large proportion of reported defects. This requires meaningful historical defect data and reliable links between defect features and real outcomes.
Option C provides the strongest training basis because defect records are linked to failed tests and production incidents. These links create useful labels and outcome evidence for learning criticality. Option A lacks historical data because the application is new and early in its first test cycle. Option B has many records, but the brand-new team reduces the relevance of people- and assignment-related patterns, and the option does not indicate that records are connected to critical outcomes. Option D is weakened by class imbalance: few critical defects and many non-critical defects make it harder for the model to learn the minority critical class.
The most effective ML setting is therefore an old application with rich, outcome-linked historical data.
References/topics: CT-AI Syllabus Chapter 11, Section 11.2 "Using AI to Analyze Reported Defects."
=========


NEW QUESTION # 115
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.

The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 0.6667.
Which metric did the testers calculate?

Answer: B

Explanation:
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)


NEW QUESTION # 116
Which of the following statements regarding experience-based testing for AI-based systems is correct?
Choose ONE option (1 out of 4)

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI- based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, OptionCaccurately reflects the role and justification of exploratory testing for AI systems.
Option A describes data analysis rather than intuitive test design. Option B is incorrect because checklist- based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines "tour-based testing"; tours refer to structured exploratory approaches, not biased datasets.
Therefore,Option Cis the syllabus-aligned correct statement.


NEW QUESTION # 117
Which of the following is a dataset issue that can be resolved using pre-processing?

Answer: D

Explanation:
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..."


NEW QUESTION # 118
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