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ISTQB CT-AI Dumps Questions Answers

Exam Code:
CT-AI
Exam Name:
ISTQB Certified Tester AI Testing Exam
Last Update: Dec 11, 2025
120 Questions Answers with Explanation Detail
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ISTQB CT-AI Last Week Results!

34

Customers Passed
ISTQB CT-AI

94%

Average Score In Real
Exam At Testing Centre

87%

Questions came word by
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CT-AI Q&A's Detail

Exam Code:
CT-AI
Total Questions:
120 Q&A's
Single Choice Questions:
119 Q&A's
Multiple Choice Questions:
1 Q&A's

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ISTQB CT-AI Exam Dumps FAQs

CT-AI stands for Certified Tester AI Testing. Its a specialist-level certification offered by ISTQB that focuses on testing AI-based systems and leveraging AI for software testing
The CT-AI exam is ideal for testers, test analysts, data analysts, test engineers, consultants, managers, developers, and anyone seeking foundational knowledge in AI testing.

Candidates must hold the ISTQB Certified Tester Foundation Level (CTFL) certificate before attempting the CT-AI exam.

Absolutely. The ISTQB CT-AI Certification is highly valuable for professionals aiming to specialize in AI testing. With AI rapidly transforming industries, this certification enhances your career prospects, credibility, and relevance in the tech space.
To pass the ISTQB CT-AI exam, you need to score at least 31 out of 47 points, which translates to a 65% passing score.
The exam is challenging but manageable with proper preparation. It covers advanced topics like machine learning, neural networks, and AI-specific testing techniques. Candidates with a solid foundation in software testing and a good grasp of AI concepts typically find it achievable.
Marks4sure offers a comprehensive CT-AI PDF questions, testing engine, and study guide tailored to the CT-AI syllabus. Our resources include CT-AI real questions, exam dumps, and practice questions to ensure your success.
We update our CT-AI practice test questions and exam dumps regularly to align with the latest ISTQB syllabus and exam trends.

ISTQB Certified Tester AI Testing Exam Questions and Answers

Questions 1

Which of the following statements about explainable AI is correct?

Choose ONE option (1 out of 4)

Options:

A.

Interpretability refers to how easily users can determine whether the result provided by the AI-based system is correct

B.

Explainability refers to how easily the algorithms and training data needed to create the model can be determined

C.

According to The Royal Society, one reason for explainable AI is to increase user confidence in the system

D.

According to The Royal Society, one reason for explainable AI is to eliminate the need for risk and vulnerability assessments

Questions 2

Which statement regarding the use of training, validation, and test data sets is correct?

Choose ONE option (1 out of 4)

Options:

A.

If only limited data is available, validation and test data sets can be combined in multiple ways during training.

B.

If limited data is available, it may be better to work without a separate test data set.

C.

Optimally, the data should be distributed equally between the training, validation, and test data sets.

D.

The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.

Questions 3

Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.

SELECT ONE OPTION

Options:

A.

Black box attacks based on adversarial examples create an exact duplicate model of the original.

B.

These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.

C.

These attacks can't be prevented by retraining the model with these examples augmented to the training data.

D.

These examples are model specific and are not likely to cause another model trained on same task to fail.

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