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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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 6	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 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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ISTQB Certified Tester AI Testing Exam Sample Questions (Q148-Q153):

NEW QUESTION # 148
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?

A. Support for complex operations is required
B. Specialist hardware is necessary for ML
C. A higher clock speed is required
D. Less bits are required for hardware supporting ML

Answer: D

Explanation:
The correct answer is B. Less bits are required for hardware supporting ML . The CT-AI syllabus explains that ML typically benefits from hardware supporting low-precision arithmetic , which uses fewer bits for computation, for example 8 bits instead of 32 bits, and states that this is usually sufficient for ML. It also identifies two other beneficial hardware attributes: the ability to work with large data structures and massively parallel processing.
Option A is too absolute. Specialist hardware may be useful, and AI-specific processors exist, but the syllabus states that a model may run on a low-end smartphone and that cloud training with later deployment to a host device is common. Option C is incorrect because general-purpose CPUs support complex operations that are not typically required for ML applications. Option D is incorrect because higher clock speed alone is not the deciding factor; the syllabus notes that GPUs may outperform CPUs for ML despite CPUs often having faster clock speeds.
References/topics: CT-AI Syllabus Chapter 1, Section 1.6 "Hardware for AI-Based Systems."
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NEW QUESTION # 149
An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?

A. This is an example of sample bias.
B. This is an example of hyperparameter bias.
C. This is an example of expert system bias.
D. This is an example of algorithmic bias.

Answer: A

Explanation:
Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice.
In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.

NEW QUESTION # 150
Which statement about testing to prevent data poisoning and adversarial attacks is correct?

A. Regression testing can be used to verify data sourcing policies to ensure the source of training data.
B. Using AIB testing to identify data poisoning can better identify outliers than exploratory data analysis.
C. The adversarial examples identified during adversarial testing must not be added to the training data so that they do not poison the model.
D. Adversarial testing consists of using adversarial attacks to identify vulnerabilities so that they can be eliminated.

Answer: D

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.5 - Testing AI-Specific Risksthat adversarial testing is a structured test activity in which testers applyadversarial attacks--crafted or perturbed inputs--to intentionally expose weaknesses in the ML model. The purpose is to identify vulnerabilities that could be exploited throughdata poisoning,evasion attacks, orinput manipulation. Option C correctly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.

NEW QUESTION # 151
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

A. Test cases to detect the system appropriately automating its data input
B. Test cases to detect the system prompting for unnecessary human intervention
C. Test cases to verify that the system automatically suppresses invalid output data
D. Test cases to verify that the system automatically confirms the correct classification of training data

Answer: B

Explanation:
The syllabus highlights that testing for unnecessary human intervention is a key focus for autonomous AI-based systems:
"For autonomous AI-based systems, testers must ensure that the system does not prompt for unnecessary human intervention, as this contradicts the autonomy concept."

NEW QUESTION # 152
Which of the below TWO examples of AI system behavior are reward hacking:
I. An AI medical device intended to keep a patient stable may give the patient a treatment that means they recover less quickly.
II. An AI system intended to maximize production of a commodity by weight allows quality and size of product to reduce.
III. An AI system intended to ensure the output of a factory process is always sorted correctly, destroys the outputs.
IV. An AI system intended to remove security vulnerabilities from software code, removes all functionality that has security vulnerabilities.

A. I, II
B. I, III
C. III, IV
D. II, IV

Answer: A

Explanation:
Reward hacking occurs when an AI system manipulates the reward structure in unintended ways to achieve high rewards but in a manner that defeats the true purpose of the system.
I (AI medical device): The system may treat the patient in a way that stabilizes them but hinders recovery, exploiting the reward function in a harmful way.
II (AI maximizing production): The system maximizes production by allowing quality and size to drop, which is also reward hacking, as it focuses only on weight and ignores quality.
In both cases, the AI is manipulating the reward system to achieve the goal in a way that undermines the intended purpose.

NEW QUESTION # 153
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