Quiz 2026 ISTQB CT-AI: Authoritative Pass Certified Tester AI Testing Exam Guide

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 4
  • 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 5
  • systems from those required for conventional systems.
Topic 6
  • 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 7
  • 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 8
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 9
  • 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 10
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q119-Q124):

NEW QUESTION # 119
Which of the following statements about ML functional performance metrics is correct?
Choose ONE option (1 out of 4)

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains ML performance metrics in Section3.2 - Evaluating ML Models.
Forclustering, which is an unsupervised learning method, the syllabus lists metrics such asintra-cluster distance,inter-cluster distance, and coherence measures. Intra-cluster metrics evaluate how close data points are within a cluster, which directly corresponds to Option A.
Option B is incorrect becauseR-squaredis a regression metric measuring goodness-of-fit, not classification performance, and has no connection to ROC curves. Option C is wrong because thesilhouette coefficientis also a clustering metric, measuring cohesion vs. separation-not regression accuracy. Option D is incorrect because ROC curves evaluatebinary or multiclass classification, not clustering.
Thus, OptionAis the only accurate statement based on the syllabus.


NEW QUESTION # 120
Which of the following neural network coverage criteria can be adapted for its application?
Choose ONE option (1 out of 4)

Answer: A

Explanation:
Section4.2 - Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network- specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered "covered." This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.
Options A and B (Sign-Sign and Sign-Change coverage) are more rigid structural criteria and are not described as adaptable within the syllabus. They focus on sign patterns of neuron activations and do not allow altering thresholds. Option D, neuron coverage, measures the proportion of neurons activated at least once.
Although simple, it is not defined as an adaptable criterion. Its limitations are documented: it provides shallow insight and too easily achieves high coverage.
Onlythreshold coverageallows testers to adjust activation thresholds for more refined coverage measurement, makingOption Cthe correct choice.


NEW QUESTION # 121
You are conducting a user acceptance test of a decision-support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?

Answer: B

Explanation:
In addition to testing the functional performance of the recommendation system, it is also important to test the amount of system resources consumed by each recommendation. This includes evaluating factors like processing time, memory usage, and the efficiency of the system when making recommendations. This ensures that the system performs well under the required load and is scalable for business use.


NEW QUESTION # 122
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?

Answer: C

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 # 123
Which data-labeling approach uses a two-step process where labeling is first done by a tool and then verified or completed by a human?
Choose ONE option (1 out of 4)

Answer: A

Explanation:
Section2.4 - Data Labeling Approachesof the ISTQB CT-AI syllabus explicitly definesAI-assisted data labelingas a hybrid process in which an automated tool performs the initial labeling and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while retaining human oversight to ensure data quality. The syllabus describes this method as an effective compromise when manual labeling alone would be too slow or costly, and when initial automation can identify obvious patterns before a human provides the final authoritative labels .
Option A (internal labeling) refers to labeling conducted by the organization's own staff but does not imply automation. Option B (crowdsourced labeling) leverages a distributed workforce, typically without automation. Option C (outsourced labeling) transfers labeling tasks to external vendors but similarly does not involve an AI-first step. Only OptionDreflects the two-stage automated-then-human workflow described in the syllabus.
Therefore,AI-assisted data labeling(Option D) is unequivocally correct.


NEW QUESTION # 124
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