100% Pass Quiz 2026 CT-AI: High-quality Certified Tester AI Testing Exam Technical Training

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Exam Format:	Multiple Choice
Related Certifications:	ISTQB CTFL ISTQB CTAL-TTA
Available Languages:	English
Exam Price:	EUR 250
Real Exam Qty:	40
Exam Duration:	60 minutes
Passing Score:	65%
Certificate Validity Period:	Lifetime (no expiration)
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

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ISTQB CT-AI Valid Practice Questions, Guaranteed CT-AI Success

Exam4Tests offers the complete package that includes all exam questions conforming to the syllabus for passing the Certified Tester AI Testing Exam (CT-AI) exam certificate in the first try. These formats of actual ISTQB CT-AI Questions are specifically designed to make preparation easier for you.

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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 (Q115-Q120):

NEW QUESTION # 115
Which statement about using AI to analyze reported defects is MOST correct?
Choose ONE option (1 out of 4)

A. ML models identify developers who should handle a defect based on ticket content.
B. ML models can identify categories for a reported defect during assignment.
C. ML models trained with critical defect tickets can identify defects that cause serious consequences.
D. ML models can support duplicate defect identification when checking defect criticality.

Answer: B

Explanation:
The ISTQB CT-AI syllabus (Section5.3 - AI Support for Defect Analysis) explains that AI can categorize defect reports using natural language processing or classification models. Categorization helps route defects efficiently and determine which areas of the system are affected. Thus, OptionCis correct: AI canidentify defect categories, supporting assignment and triage.
Option A is incorrect because ML cannot infer severity or consequence without domain-specific risk modeling. Option B inaccurately ties duplicate detection to criticality; the syllabus separatesduplicate detectionfromseverity analysis. Option D is incorrect because AI may assist in routing defects using categories, but the syllabus does not claim AI identifies specific developers.
Thus,Option Cis the syllabus-accurate statement.

NEW QUESTION # 116
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION

A. Maximize recall and precision
B. Maximize precision and accuracy
C. Maximize accuracy and recall
D. Maximize specificity number of classes

Answer: A

Explanation:
* Prevalence Rate and Model Performance:
* The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
* Importance of Recall:
* Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
* Importance of Precision:
* Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
* Balancing Recall and Precision:
* In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
* Accuracy and Specificity:
* While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
* Conclusion:
* Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.
This explanation aligns with the principles outlined in the ISTQB CT-AI Syllabus, particularly sections on performance metrics for ML models and handling imbalanced datasets (Chapter 5: ML Functional Performance Metrics).

NEW QUESTION # 117
Which ONE of the following statements is MOST true in relation to A/B testing?

A. Production data is not used
B. The purpose is to detect defects
C. The purpose is to compare two variants of a system
D. Many test cases can be generated

Answer: C

Explanation:
The correct answer is D . The CT-AI syllabus defines A/B testing as a method where the responses of two variants of a program, A and B, are compared using the same inputs to determine which variant is better. It is a statistical testing approach that typically compares results across several runs. The syllabus also states that a major difference between A/B testing and back-to-back testing is that A/B testing compares two variants of the same system, whereas back-to-back testing is used to detect defects.
Option A is incorrect because A/B testing does not generate test cases and gives no guidance on how tests should be designed, although operational inputs may be used. Option B is more characteristic of back-to-back testing, which uses a pseudo-oracle for defect detection. Option C is incorrect because production or operational inputs are often used, and the syllabus gives examples of e-commerce and web companies using A
/B testing in production to compare consumer responses. Therefore, comparison of two system variants is the defining feature.
References/topics: CT-AI Syllabus Chapter 9, Section 9.4 "A/B Testing"; Section 9.7 "Selecting Test Techniques for AI-Based Systems."

NEW QUESTION # 118
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?

A. Robustness
B. Simplicity
C. Sustainability
D. Non-determinism

Answer: D

Explanation:
The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI- based systems:
"The characteristics of AI-based systems that make it more difficult to ensure they are safe...
include:
complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, and lack of robustness."

NEW QUESTION # 119
Which ONE of the following statements about a system MOST describes 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 self-driving car that automatically stops the car if the driver is not responding to voice prompts, in order to ensure they are awake.
D. 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.

Answer: C

Explanation:
An autonomous system is capable of performing tasks independently, without requiring human intervention. The self-driving car in option B is an example of an autonomous system because it automatically takes action (stopping the car) based on its internal decision-making, in response to the driver's behavior.

NEW QUESTION # 120
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