[Q54-Q71] Excellent CT-AI PDF Dumps With 100% ValidExam Exam Passing Guaranted [Aug-2026]

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Excellent CT-AI PDF Dumps With 100% ValidExam Exam Passing Guaranted [Aug-2026]

100% Pass Your CT-AI Certified Tester AI Testing Exam at First Attempt with ValidExam

ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester AI Testing (CT-AI) Exam
Exam Number:	CT-AI
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Exam Format:	Multiple Choice
Real Exam Qty:	40
Available Languages:	English
Certificate Validity Period:	Lifetime
Passing Score:	65%
Exam Duration:	60 minutes
Recommended Training:	ISTQB Accredited Training Providers
Exam Registration:	ISTQB Official Website
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online and onsite proctored exam via accredited ISTQB examination providers
Pre Condition:	Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals.
Official Syllabus URL:	https://www.istqb.org

QUESTION 54
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

The data pipeline

The quality of the labeling

Biased data

The number of classes

QUESTION 55
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.

The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 0.6667.
Which metric did the testers calculate?

F1-score

Precision

Recall

Accuracy

QUESTION 56
Which ONE of the following requirements for an AI based system is MOST likely to cause a challenge for testing?

The system should be able to accept natural language input on a variety of topics and respond correctly

The system should respond more quickly than the system it is replacing

The system should be 100% accurate with no tolerance for failure

The system should not require human intervention until a specific failure scenario is encountered

QUESTION 57
You are conducting a user acceptance test of a decision-support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?

The number of inaccurate recommendations per day

The amount of system resources consumed by each recommendation

The number of accurate recommendations per day

The accuracy of the human decision when presented with an inaccurate recommendation

QUESTION 58
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

Al systems are inherently flexible.

Al systems require changing of operational environments; therefore, flexibility is required.

Flexible Al systems allow for easier modification of the system as a whole.

Self-learning systems are expected to deal with new situations without explicitly having to program for it.

QUESTION 59
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

Al systems are inherently flexible.

Al systems require changing of operational environments; therefore, flexibility is required.

Flexible Al systems allow for easier modification of the system as a whole.

Self-learning systems are expected to deal with new situations without explicitly having to program for it.

QUESTION 60
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?

Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.

Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

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.

Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

QUESTION 61
Which statement about using AI to analyze reported defects is MOST correct?
Choose ONE option (1 out of 4)

ML models trained with critical defect tickets can identify defects that cause serious consequences.

ML models can support duplicate defect identification when checking defect criticality.

ML models can identify categories for a reported defect during assignment.

ML models identify developers who should handle a defect based on ticket content.

QUESTION 62
Which ONE of the below is MOST likely to indicate a problem with underfitting in an ML model?

The model is vulnerable to adversarial attacks

The model fails to generalize on new data

The model uses a large amount of resources to make a prediction

The model is inaccurate on data similar to the training data

QUESTION 63
Which of the following neural network coverage criteria can be adapted for its application?
Choose ONE option (1 out of 4)

Sign-Sign coverage

Sign-Change coverage

Threshold coverage

Neuron coverage

QUESTION 64
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION

Different Road Types

Different weather conditions

ML model metrics to evaluate the functional performance

Different features like ADAS, Lane Change Assistance etc.

Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options isleast likelyto be a reason for the explosion in the number of parameters.
Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car’s system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car’s sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.

QUESTION 65
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

The feedback requires a physical connection and cannot be sent over the Internet.

Mobile operating systems cannot process machine learning algorithms.

The size of the application is consuming too much of the phone’s storage capacity.

The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.

QUESTION 66
“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?

Maximize precision and accuracy

Maximize accuracy and recall

Maximize recall and precision

Maximize specificity number of classes

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.

QUESTION 67
Which option gives the correct values for accuracy and precision from the confusion matrix?
Choose ONE option (1 out of 4)

Accuracy = 50%, Precision = 75%

Accuracy = 80%, Precision = 75%

Accuracy = 75%, Precision = 80%

Accuracy = 80%, Precision = 50%

QUESTION 68
Which ONE of the below types of testing is NOT a type of experience-based testing applied to an AI-based system?

Exploring training data to understand its variety, patterns, structure and shape – then using that to inform testing

Usability testing of the format of the output predictions

Using knowledge about previous ML systems to identify potential biases

Using an industry checklist to assess the steps used to prepare an ML system

QUESTION 69
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?

It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.

It could modify the automation code to ignore unrecognizable objects to avoid failures.

It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.

It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.

QUESTION 70
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I – V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?

Aspects II, III and IV

Aspects I, II, and III

Aspects III, IV, and V

Aspects I, IV, and V

QUESTION 71
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

Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.

Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

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	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 3	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 4	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.
Topic 5	systems from those required for conventional systems.
Topic 6	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 7	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.