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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Certificate Validity Period:	Lifetime (no expiration)
Exam Price:	EUR 250
Real Exam Qty:	40
Exam Duration:	60 minutes
Available Languages:	English
Exam Format:	Multiple Choice
Related Certifications:	ISTQB CTFL ISTQB CTAL-TTA
Passing Score:	65%
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
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	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 3	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 4	systems from those required for conventional systems.
Topic 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6	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 7	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 8	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
You are evaluating the use of a highly accurate pre-trained model that is used widely in industry for a similar use case. There is an intention to apply transfer learning techniques to further customize 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 system may be vulnerable to adversarial attacks inherited from the pre-trained model
C. The functional performance of the pre-trained model could be lower than stakeholders expect
D. The functional performance of the system may exhibit bias

Answer: C

Explanation:
When using a pre-trained model, especially one that is highly accurate and widely used in industry, it is less likely that the functional performance will be lower than expected, given that it has already been proven effective in similar use cases.

NEW QUESTION # 52
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 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 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 # 53
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not. The bank has enough data on past customers to randomly split the data into a training data set and a test/validation data set. A logistic regression model is constructed on the training data set using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

A. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present.
B. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
C. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set, if the two models significantly differ, it will indicate there is bias in the original model.
D. Experienced-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set.

Answer: D

Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model."

NEW QUESTION # 54
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)

A. When testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis
B. Automation bias affects the testing of AI-based systems that support users in their actions or decisions
C. Automation bias particularly affects testing of autonomous systems
D. Automation bias is tested with representative users, but human input quality is irrelevant

Answer: B

Explanation:
Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.
Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.
Thus,Option Bis the syllabus-accurate description of automation bias.

NEW QUESTION # 55
Which statement regarding flexibility and adaptability of AI-based systems is correct?
Choose ONE option (1 out of 4)

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

Answer: B

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 # 56
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