CT-AI Valid Test Online - CT-AI Study Materials Review

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ISTQB Certified professionals are often more sought after than their non-certified counterparts and are more likely to earn higher salaries and promotions. Moreover, cracking the Certified Tester AI Testing Exam (CT-AI) exam helps to ensure that you stay up to date with the latest trends and developments in the industry, making you more valuable assets to your organization.

ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Exam Duration:	60 minutes
Passing Score:	65%
Certificate Validity Period:	Lifetime (no expiration)
Available Languages:	English
Exam Format:	Multiple Choice
Real Exam Qty:	40
Exam Price:	EUR 250
Related Certifications:	ISTQB CTAL-TTA ISTQB CTFL
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

>> CT-AI Valid Test Online <<

Free PDF 2026 CT-AI: Latest Certified Tester AI Testing Exam Valid Test Online

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 4	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 5	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 6	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 7	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 8	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 9	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 10	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?

A. Threshold coverage
B. Neuron coverage
C. Value coverage
D. Sign change coverage

Answer: D

Explanation:
Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.

NEW QUESTION # 47
Which ONE of the following statements about a system MOST describe an autonomous system?

A. A chatbot that learns the most effective responses to humans (in order to ensure the humans continue to converse) based on prior experiences.
B. A fraud detection system that alerts operators when it sees a specific fraud risk associated with a transaction, in order to obtain human input to make a final decision.
C. A loan approval system that can continue to process loan applications indefinitely, within a defined amount of aggregate credit, until an operator reduces the amount of aggregate credit available.
D. A self driving car that automatically stops the car if the driver is not responding to voice prompts, in order to ensure they are awake.

Answer: C

Explanation:
The correct answer is A because it best matches the CT-AI concept of autonomy: the system can operate independently for a prolonged period, processing loan applications within predefined constraints until a human changes the available aggregate credit. The syllabus defines autonomy as the ability of a system to work independently of human oversight and control for prolonged periods, and emphasizes that testers should identify how long the system is expected to perform satisfactorily without human intervention and when control should be returned to humans.
Option B describes a safety or driver-monitoring function rather than sustained independent operation; the system reacts when the driver is unresponsive but still assumes human supervision. Option C describes evolution or self-learning, because the chatbot improves responses based on prior interactions, but self- learning alone does not make the system autonomous. Option D is explicitly human-in-the-loop: the fraud system only alerts operators and requires human input for the final decision, so it is less autonomous.
References/topics: CT-AI Syllabus Chapter 2, Section 2.2 "Autonomy"; Appendix B "Autonomous system" and "Autonomy."

NEW QUESTION # 48
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION

A. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
B. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
C. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
D. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

Answer: D

Explanation:
* A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
* B. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
* Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
* C. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
* This approach directly compares the performance of two implementations of the same algorithm.
If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
* D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, optionCis the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.

NEW QUESTION # 49
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labelling approaches can be used?

A. Benchmarking
B. Augmentation
C. Internal
D. Annotation

Answer: D

Explanation:
The correct answer is B. Annotation . The CT-AI syllabus explains that data labelling enriches unlabeled or poorly labelled data by adding labels so that it becomes suitable for supervised learning. For image-based supervised learning, the syllabus specifically describes labelling objects in images by drawing rectangles around them as a common labelling technique, known as annotation . It also notes that more complex annotation may be required for 3D objects or irregular bounding boxes, and that data labelling and annotation are typically supported by tools.
This directly matches the football-game scenario: the ML engineer must identify the location of the football in each image, which requires object localization rather than merely assigning the whole image to a class.
Option A, "internal," describes who performs labelling within an organization, not the image-labelling technique itself. Option C, augmentation, creates additional data points from an existing dataset, such as transformed images, but it does not identify the football's location. Option D, benchmarking, is unrelated to labelling and is used for comparative evaluation.
References/topics: CT-AI Syllabus Chapter 4, Section 4.5 "Data Labelling for Supervised Learning"; Appendix B "Annotation."

NEW QUESTION # 50
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting testing data from a boat manufacturer's bolt longevity data
B. Selecting training data purposely excluding specific faulty conditions
C. Selecting testing data from a different dataset than the training dataset
D. Selecting training data by purposely including all known faulty conditions

Answer: B

Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)

NEW QUESTION # 51
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