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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 May. 26, 2026 (see below)
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)
Which of the following are the three activities in the data acquisition activities for data preparation?
The syllabus defines data acquisition as consisting of three steps:
''Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data.''
(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1, page 33 of 99)
Which of the following is a dataset issue that can be resolved using pre-processing?
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...'
(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1.1, Page 34 of 99)
Which statement regarding pairwise testing in an AI-based automotive lane-keeping assist system is correct?
Choose ONE option (1 out of 4)
The ISTQB CT-AI syllabus (Section4.3 -- Test Design for AI-Based Systems) highlights pairwise testing as an effectivetest-case reduction techniquefor systems with many input parameters. Lane-keeping assist systems typically include environmental, sensor, and vehicle-dynamic parameters, making exhaustive testing infeasible. Pairwise testing significantly reduces the number of test cases while still capturingall 2-way interactions, which are responsible for a large proportion of software defects.
OptionBaligns with this syllabus description: pairwise testing reduces otherwise extremely large parameter combinations, making test effort manageable.
Option A overstates feasibility guarantees; the syllabus never claims pairwise testing always makes testing ''typically feasible.'' Option C is unsupported and incorrect because pairwise testing doesnotrestrict parameters to driver-controlled ones. Option D is incorrect because, although some defects arise from higher-order interactions, pairwise testing captures many relevant defects and is widely recognized as a pragmatic compromise.
Thus,Option Bis the correct statement.
Which statement about AI-based test case generation is correct?
Choose ONE option (1 out of 4)
The ISTQB CT-AI syllabus indicates inSection 5.2 -- AI for Testingthat AI-generated test cases may not come withpredefined expected results. This is because test-case generation methods---such as evolutionary algorithms, reinforcement learning, or clustering-based sampling---produceinputs, but the tester must still determine the correct outputs. Therefore, OptionCis correct: expected results may not be available, especially when AI produces novel or previously unseen input combinations.
Option A is incorrect: AI-based test generation can improve coverage by exploring large input spaces. Option B is incorrect because the need for oracles does not depend on whether the test case is AI-generated. Option D is incorrect because the syllabus allows using a model as an oracle inback-to-back testing, provided limitations are understood.
Therefore,Option Cis the correct syllabus-aligned statement.
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