ISTQB ISTQB Certified Tester AI Testing Exam CT-AI Exam Dumps: Updated Questions & Answers (December 2025)

 Labelling Approaches:Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements​​.

The syllabus mentions that experience-based testing and EDA are effective for detecting biases:

“Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)

According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:

"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)

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)

 Type of Testing for Stock-Price Prediction Models:Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors​​.

The syllabus mentions a drawback of AI-generated test cases:

“AI-based test generation tools can generate test cases… However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)

Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:

"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)

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 bemodifiedto 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 determiningwhenandhowto adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with OptionA.

Option B reverses definitions—the syllabus states flexibility (not adaptability) relates to unspecified situations. Option C is incorrect: self-learning systems requirebothflexibility and adaptability; they are not categorized as one or the other. Option D incorrectly defines flexibility; the syllabus defines adaptability—not flexibility—as ease of modification.

Thus,Option Acorrectly reflects the syllabus.

The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:

"Even the use of pairwise testing can result in extensive test suites… automation and virtual test environments often become necessary to allow the required tests to be run."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)

The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:

"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.3, page 67 of 99)