iSQI Certified Tester AI Testing CT-AI Exam Questions

Answer : D

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.

Answer : A, C

The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to-back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.

Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 -- Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as ''if customer income increases, risk rating should not worsen.'' These relations allow validation even when exact expected outputs are unavailable.

Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.

Thus,A and Care the correct and syllabus-supported choices.

Answer : C

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.

Answer : A

The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:

'Lack of required domain knowledge may lead to incorrect labelling.'

(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2, page 38 of 99)

Answer : A

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.''

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1, Page 26 of 99)

Answer : A

The ISTQB CT-AI syllabus definesoverfittingin Section3.2 -- ML Model Evaluationas a condition where an ML model learns the training data too precisely---including noise and irrelevant detail---resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .

Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involvestransfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.

Thus,Ais the correct and syllabus-aligned example of overfitting.

Answer : A

TheISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 -- AI Technologies. Within this section, AI methods are grouped into categories, one of which is''Reasoning techniques.''These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.

Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.

The other options---Linear regression,Random Forest, andGenetic algorithms---are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.

Thus, based on the authoritative definitions in the syllabus,Deductive classifiers (Option A)is the only technology classified as a reasoning technique.