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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	systems from those required for conventional systems.
Topic 3	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 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	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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100% Pass 2026 ISTQB CT-AI: Latest Exam Certified Tester AI Testing Exam Tips

The Certified Tester AI Testing Exam (CT-AI) certification test is an important part of career growth, and passing it may lead to more employment opportunities. However, preparing for the Certified Tester AI Testing Exam (CT-AI) test may be tough, and many busy applicants have difficulty cracking it. This is where ITexamReview Certified Tester AI Testing Exam real exam questions come to help you clear the test in a short time.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)

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

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 aregression problem. 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 # 60
Which statement regarding flexibility and adaptability of AI-based systems is correct?
Choose ONE option (1 out of 4)

A. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
B. Adaptability is considered to be the ability of the system to be used in unspecified situations.
C. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.
D. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.

Answer: D

Explanation:
The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 - Flexibility and Adaptability.
Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can bemodifiedto handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself- learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determiningwhenandhowto adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with OptionA.
Option B reverses definitions-the syllabus states flexibility (not adaptability) relates to unspecified situations. Option C is incorrect: self-learning systems requirebothflexibility and adaptability; they are not categorized as one or the other. Option D incorrectly defines flexibility; the syllabus defines adaptability-not flexibility-as ease of modification.
Thus,Option Acorrectly reflects the syllabus.

NEW QUESTION # 61
Which ONE of the following describes a situation of back-to-back testing the LEAST?

A. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
B. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
C. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
D. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

Answer: D

Explanation:
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions.
Let's analyze the options given:
A). Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B). Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C). Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D). Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to- back testing.

NEW QUESTION # 62
You are evaluating the use of a highly accurate pre trained model that is used widely in industry tor a similar use case. There is an intention to apply transfer learning techniques to further customise the model. Which ONE of the following is the LEAST likely to be a significant risk with this approach?

A. Differences in data preparation steps between the training of the pre-trained model and consequent use of the model may result in reduced functional performance
B. The functional performance of the system may exhibit bias
C. The functional performance of the pre trained model could be lower than stakeholders expect
D. The system may be vulnerable to adversarial attacks inherited from the pre trained model

Answer: C

Explanation:
The correct answer is D because the scenario explicitly reduces that risk: the model is described as highly accurate , widely used in industry, and applied to a similar use case. The CT-AI syllabus explains that using a pre-trained model can save training costs and largely eliminate the risk of it not working when an existing suitable model is reused. It also states that transfer learning effectiveness depends heavily on similarity between the original model's function and the new required function.
Options A, B, and C remain significant risks under the syllabus. A is credible because shortcomings such as inherited biases may pass from the pre-trained model to the reused system. B is credible because models created through transfer learning are highly likely to remain sensitive to vulnerabilities of the pre-trained model, including adversarial attacks. C is credible because differences in data preparation steps between original model development and later use may reduce functional performance. Therefore, the least likely significant risk is unexpectedly low functional performance of the already highly accurate, widely used, similar-purpose pre-trained model.
References/topics: CT-AI Syllabus Chapter 1, Sections 1.8.1-1.8.3 "Pre-Trained Models," "Transfer Learning," and "Risks of using Pre-Trained Models and Transfer Learning."
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NEW QUESTION # 63
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?

A. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
B. Individual bias at the neuron level, and activation values of neurons in the previous layer.
C. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
D. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Answer: C

NEW QUESTION # 64
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