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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	systems from those required for conventional systems.
Topic 5	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 6	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 7	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 8	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 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	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 11	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q113-Q118):

NEW QUESTION # 113
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

A. Designing a reward function
B. Functional performance
C. The time and associated costs requited
D. Specification of acceptance criteria

Answer: C

Explanation:
The correct answer is A . The CT-AI syllabus identifies data preparation as one of the most resource-intensive parts of the ML workflow, stating that it uses an average of 43% of ML workflow effort. It also explains that data preparation forms part of the data pipeline and includes acquisition, pre-processing, feature engineering, and related exploratory analysis.
The syllabus lists several data preparation challenges, including the need for domain and data knowledge, difficulty obtaining high-quality data from multiple sources, difficulty automating a scalable and efficient production data pipeline, costs associated with data preparation, defects introduced into the data pipeline, and sample bias.
Option B, functional performance, is a result or evaluation concern for the ML model rather than a data preparation challenge itself. Option C, designing a reward function, is specifically a reinforcement learning challenge. Option D, specifying acceptance criteria, belongs primarily to understanding objectives and defining evaluation targets, not the data preparation activity.
References/topics: CT-AI Syllabus Chapter 4, Section 4.1 "Data Preparation as Part of the ML Workflow"; Section 4.1.1 "Challenges in Data Preparation."
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NEW QUESTION # 114
Which statement about using AI to analyze reported defects is MOST correct?
Choose ONE option (1 out of 4)

A. ML models can support duplicate defect identification when checking defect criticality.
B. ML models can identify categories for a reported defect during assignment.
C. ML models trained with critical defect tickets can identify defects that cause serious consequences.
D. ML models identify developers who should handle a defect based on ticket content.

Answer: B

Explanation:
The ISTQB CT-AI syllabus (Section5.3 - AI Support for Defect Analysis) explains that AI can categorize defect reports using natural language processing or classification models. Categorization helps route defects efficiently and determine which areas of the system are affected. Thus, OptionCis correct: AI canidentify defect categories, supporting assignment and triage.
Option A is incorrect because ML cannot infer severity or consequence without domain-specific risk modeling. Option B inaccurately ties duplicate detection to criticality; the syllabus separatesduplicate detectionfromseverity analysis. Option D is incorrect because AI may assist in routing defects using categories, but the syllabus does not claim AI identifies specific developers.
Thus,Option Cis the syllabus-accurate statement.

NEW QUESTION # 115
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?

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

Answer: B

Explanation:
The syllabus explains the fundamental distinction between conventional systems and AI-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 patterns from 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.

NEW QUESTION # 116
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 generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
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 modify the automation code to ignore unrecognizable objects to avoid failures.
D. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.

Answer: D

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 # 117
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?

A. Error-shortcircuiting
B. Reward-hacking
C. Interpretability
D. Transparency

Answer: B

Explanation:
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function-driving backwards to avoid bumper triggers while maximizing reward for speed.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)

NEW QUESTION # 118
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