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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	systems from those required for conventional systems.
Topic 2	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 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	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 5	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 6	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 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	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 9	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 (Q91-Q96):

NEW QUESTION # 91
Which statement regarding flexibility and adaptability of AI-based systems is correct?

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

Answer: C

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 be modified to 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 determining when and how to adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with Option A.

NEW QUESTION # 92
Which challenge to testing self-learning systems puts you at risk of a data attack?
Choose ONE option (1 out of 4)

A. Complex test environment
B. Inadequate specification of the operating environment
C. Insufficient testing time
D. Unexpected changes

Answer: D

Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift.
The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. OptionD - Unexpected changescorresponds directly to this syllabus-defined risk.
Option A refers to system specification issues but does not relate to data attacks. Option B discusses environment complexity, which makes testing difficult but is not tied to adversarial threats. Option C (insufficient testing time) affects quality but does not specifically increase vulnerability to malicious data manipulation.
Unexpected changes-including data drift, poisoned samples, or maliciously constructed training data-pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, causing model degradation or manipulation. Therefore,Option Dcorrectly identifies the challenge that increases exposure to data attacks.

NEW QUESTION # 93
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI based systems?

A. Inputs to the system and environment factors can be considered parameters for pairwise tests
B. Each neuron can be treated as a parameter for pairwise tests
C. Combinatorial testing cannot yet be applied to AI
D. Two variants of the system can be used and compared

Answer: A

Explanation:
The correct answer is C . The CT-AI syllabus explains that AI-based systems can have extremely high numbers of parameters, especially where they use big data or interact with the external world. Exhaustive testing of all parameter combinations is usually infeasible, so combinatorial techniques, especially pairwise testing, can reduce the number of required test cases while preserving useful defect detection capability.
The syllabus gives autonomous driving as an example, where high-level system tests should consider different operational environments and vehicle functions. Relevant parameters may include road types, surfaces, weather, traffic conditions, visibility, vehicle functions, and sensor effectiveness. This directly supports treating system inputs and environmental factors as parameters in pairwise tests.
Option A is not the intended application; neuron coverage is a neural-network coverage topic, not pairwise system scenario design. Option B describes A/B testing or back-to-back comparison, not combinatorial testing. Option D is false because the syllabus explicitly describes pairwise testing for AI-based systems and includes it as an examinable method.
References/topics: CT-AI Syllabus Chapter 9, Section 9.2 "Pairwise Testing."
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NEW QUESTION # 94
Which of the following problems would best be solved using the supervised learning category of regression?

A. Determining if an animal is a pig or a cow based on image recognition.
B. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.
C. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.
D. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.

Answer: B

Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."

NEW QUESTION # 95
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. In classification, objects are always assigned to exactly two classes
C. Predicting that diesel prices will increase by ~10% is a classification problem
D. Deciding whether an object is a bicycle or a motorcycle is a classification problem

Answer: D

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 aregressionproblem. 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 # 96
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