Pass the ISTQB AI Testing CT-AI Questions and answers with Dumpstech

The ISTQB CT-AI syllabus (Section5.2 – AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods—one of the three major AI technique groups—are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with OptionB.

Option A is incorrect because regression test optimization is typically performed usingsearch-based optimization, not classification. Option C is incorrect because fuzzy logic is more suited to reasoning under vagueness, not generating test cases. Option D is incorrect: defect prediction relies on statistical learning or classification models, not computational optimization.

Thus,Option Bis the most syllabus-consistent mapping of AI techniques to testing tasks.

The syllabus states:

“When testing probabilistic and non-deterministic systems, the same input may produce different outputs. Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)

The syllabus describes automation bias as:

"A type of bias caused by a person favoring the recommendations of an automated decision-making system over other sources."

This is also known as complacency bias, where testers accept automated system outputs without questioning them.

(Reference: ISTQB CT-AI Syllabus v1.0, Glossary, Page 89 of 99)

The syllabus explicitly mentions challenges of handling personal data and ensuring privacy when testing AI-based systems:

"The management of personal data and sensitive data is often a concern during testing, as testing typically requires realistic data and it is difficult to fully anonymize data."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.3, page 52 of 99)

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)

 Factors Affecting ML Functional Performance:The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics​​​​.

Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options isleast likelyto be a reason for the explosion in the number of parameters.

Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.

Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.

ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.

Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.

Hence, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.

In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:

"Evolution is the system’s ability to change itself to adapt to new situations, different hardware, or a changing operational environment."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3)

The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:

"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

The ISTQB CT-AI syllabus (Section3.2 – Model Evaluation) specifies the correct usage oftraining,validation, andtestdatasets. It emphasizes that thetest dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.

Option A contradicts the syllabus because validation and test sets servedifferent purposes: validation is for tuning, test is for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test set or using techniques such ascross-validationrather than eliminating testing. Option C is wrong because equal distribution (33/33/33) isnot recommended; typically, the training set is much larger (e.g., 70–80%).

Thus, OptionDis the only statement aligned with the syllabus' guidance.