Selected CT-AI ISTQB AI Testing Questions Answers

A. Regression

Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.

B. Association

Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.

C. Clustering

Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.

D. Classification

Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.

Therefore, the correct answer isCbecause clustering is the most suitable method for grouping people with similar tastes for targeted product marketing​​.

The syllabus describes exploratory testing as especially useful when there are poor specifications or test oracle problems:

“Exploratory testing is especially useful when there are poor specifications or test oracle problems, which is often the case for AI-based systems.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.6, page 70 of 99)

The syllabus states:

"Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined."

Access to the training data is an example of transparency.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7, page 24 of 99)

Section5.3 – AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defectswhen new changes resemble previous defect-inducing patterns. This directly supports OptionA, which states that defect prediction is most effective when based on previous similar constellations.

Option B is incorrect: ML can predictwhich componentsare likely to fail, not only whether defects exist. Option C is incomplete; code metrics help, but defect prediction relies onmanycontextual features (historical defects, code churn, commit frequency, etc.). Option D is wrong because defect prediction isnotbased on formal principles and typically requiresmany features, not just a few.

Thus,Option Ais the correct and syllabus-consistent answer.