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ISTQB CT-AI Exam With Confidence Using Practice Dumps

Exam Code:
CT-AI
Exam Name:
ISTQB Certified Tester AI Testing Exam
Certification:
ISTQB AI Testing
Vendor:
ISTQB
Questions:
120
Last Updated:
Feb 11, 2026
Exam Status:
Stable
ISTQB CT-AI

CT-AI: ISTQB AI Testing Exam 2025 Study Guide Pdf and Test Engine

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ISTQB Certified Tester AI Testing Exam Questions and Answers

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Question 1

There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

Options:

A.

Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions

B.

Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers

C.

Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes

D.

Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas

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Question 2

Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

Options:

A.

It has been trained to recognize cats and is given an image of a dog

B.

It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution

C.

It has been trained to analyze mathematical models and is given a set of landscape pictures to classify

D.

It has been trained to analyze customer buying trend data and is given information on supplier cost data

Question 3

Which of the following descriptions of quality aspects of a data set is correct?

Choose ONE option (1 out of 4)

Options:

A.

The quality aspect "Incomplete data" describes the fact that data is missing, e.g., for a certain time interval.

B.

The quality aspect "Data not preprocessed" describes the fact that the collected data was recorded incorrectly.

C.

The quality aspect "Irrelevant data" describes the fact that irrelevant data does not affect the ML model.

D.

The quality aspect "Unbalanced data" describes the fact that the data used should be as up-to-date as possible.

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