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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	systems from those required for conventional systems.
Topic 7	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.

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100% Pass Quiz ISTQB - CT-AI - Latest Latest Certified Tester AI Testing Exam Practice Materials

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?

A. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
B. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
C. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
D. It could modify the automation code to ignore unrecognizable objects to avoid failures.

Answer: A

Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."

NEW QUESTION # 64
Which ONE of the below is NOT an input to an activation function in a neural network?

A. Bias
B. Activation values form the current layer
C. Activation values from the previous layer
D. The weights assigned to the connections

Answer: B

Explanation:
The correct answer is C . In a neural network, each neuron calculates its activation value using an activation function. The CT-AI syllabus explains that the activation function receives as input the activation values from all neurons in the previous layer , the weights assigned to the connections between neurons, and the individual bias of the neuron.
Therefore, options A, B, and D are valid inputs to the activation function. Bias is a preset constant associated with the neuron. The activation values from the previous layer represent the incoming signal. The weights assigned to the connections determine how strongly those incoming activation values influence the neuron's calculation. Option C is not an input because activation values from the current layer are the outputs of activation functions being computed in that layer, not inputs to the same computation. In feed-forward neural network logic, information passes from the input layer through hidden layers toward the output layer; each layer computes its activation values based on the previous layer, not on activation values simultaneously generated in the current layer.
References/topics: CT-AI Syllabus Chapter 6, Section 6.1 "Neural Networks"; Appendix B "Activation function" and "Activation value."
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NEW QUESTION # 65
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?

A. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
B. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
C. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
D. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

Answer: C

Explanation:
This approach directly compares the performance of two implementations of the same algorithm.
If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.

NEW QUESTION # 66
A system is to be developed to detect lung cancer using X-ray images.
Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?
Choose ONE option (1 out of 4)

A. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
B. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.
C. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.
D. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.

Answer: C

Explanation:
The syllabus explains the fundamental distinction betweenconventional systemsandAI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems. A conventional system relies on human-programmed logic-such as branches, conditions, and explicit rules-to interpret input data.
The system behaves exactly as specified by its developers.
In contrast,AI systems using supervised learning automatically learn patternsfrom labeled data. The syllabus states that"patterns in data are used by the system to determine how it should react in the future...
The AI determines on its own what patterns or features in the data can be used". This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.
Option A is incorrect because AI outputs are typicallylessexplainable, not more. Option B is incorrect because both systems can use thesame X-ray images; ML does not require structurally different images. Option D is oversimplified and not fully accurate; while training data is central to ML, AI systems also include architecture, algorithms, and preprocessing-not just data.
Thus,Option Cis the correct and syllabus-aligned answer.

NEW QUESTION # 67
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

A. Clustering is supervised learning.
B. Clustering is done without prior knowledge of output classes.
C. Clustering is classification of a continuous quantity.
D. Clustering requires you to know the classes.

Answer: B

Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.

NEW QUESTION # 68
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