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iSQI CT-AI Exam Questions
- Topic 1: 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 2: 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 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: 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 5: 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 6: 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 7: 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 8: 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 9: 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 10: Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based systems from those required for conventional systems.
- Topic 11: Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Free iSQI CT-AI Exam Actual Questions
Note: Premium Questions for CT-AI were last updated On Sep. 11, 2026 (see below)
Which assignment of AI techniques to testing support is BEST?
Choose ONE option (1 out of 4)
The ISTQB CT-AI syllabus (Section5.2 -- AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods---one of the three major AI technique groups---are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with OptionB.
Option A is incorrect because regression test optimization is typically performed usingsearch-based optimization, not classification. Option C is incorrect because fuzzy logic is more suited to reasoning under vagueness, not generating test cases. Option D is incorrect: defect prediction relies on statistical learning or classification models, not computational optimization.
Thus,Option Bis the most syllabus-consistent mapping of AI techniques to testing tasks.
Which statement about testing levels for AI-based systems is correct?
Choose ONE option (1 out of 4)
Section4.3 -- Test Levels for AI Systemsclearly defines ML model testing as the level at which testers evaluate whether an ML model fulfills itsfunctional performance criteria, including accuracy, precision, recall, F1, robustness, stability, and fairness. Therefore, Option C is the correct and syllabus-aligned statement.
Option A is incorrect because input data testing focuses onvalidity and correctness of data entering the model, not interactions with all system components. Option B is incorrect: acceptance testing in the syllabus focuses primarily onbusiness and stakeholder requirements, not specifically explainability. Explainability testing may occur at multiple levels depending on context. Option D is also incorrect because API testing belongs tointegration testing, not system testing, even when AI is consumed as a service.
Thus,Option Cis the only statement that precisely matches syllabus definitions.
Which of the following is correct regarding the layers of a deep neural network?
The syllabus clearly explains the structure of a deep neural network (DNN):
'A deep neural network comprises three types of layers. The input layer receives inputs... Between the input and output layers are hidden layers made up of artificial neurons, which are also known as nodes.'
(Reference: ISTQB CT-AI Syllabus v1.0, Section 6.1, page 45 of 99)
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I .Autonomy
II .Maintainability
III .Safety
IV .Transparency
V .Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network, the shortest path indicates a "buy" and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?
The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:
'Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold.'
(Reference: ISTQB CT-AI Syllabus v1.0, Section 6.2, page 48 of 99)
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