ISTQB CT-AI Exam With Confidence Using Practice Dumps

The ISTQB CT-AI syllabus explains inSection 4.5 – Testing AI-Specific Risksthat adversarial testing is a structured test activity in which testers applyadversarial attacks—crafted or perturbed inputs—to intentionally expose weaknesses in the ML model. The purpose is to identify vulnerabilities that could be exploited throughdata poisoning,evasion attacks, orinput manipulation. OptionCcorrectly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.

Option A is incorrect because regression testing does not verify data sourcing policies; it verifies unchanged functionality after modifications. Option B is incorrect because adversarial examples are oftenaddedto training datasets to improverobustness(a practice called adversarial training), not excluded. Option D is incorrect because AIB testing is not described as superior to exploratory data analysis in outlier detection; both have different purposes, and EDA remains essential for data quality assessment.

Thus,Option Cis consistent with syllabus-defined adversarial testing.

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.

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)