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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)
Certificate Validity Period:	Lifetime
Real Exam Qty:	40
Available Languages:	English
Exam Format:	Multiple Choice
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

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	systems from those required for conventional systems.
Topic 4	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 5	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.
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	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 8	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 9	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 10	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 11	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 (Q31-Q36):

NEW QUESTION # 31
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers?

A. These examples are model specific and are not likely to cause another model trained on same task to fail.
B. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
C. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
D. Black box attacks based on adversarial examples create an exact duplicate model of the original.

Answer: A

Explanation:
Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.

NEW QUESTION # 32
You are evaluating the use of a highly accurate pre-trained model that is used widely in industry for a similar use case. There is an intention to apply transfer learning techniques to further customize the model.
Which ONE of the following is the LEAST likely to be a significant risk with this approach?

A. The system may be vulnerable to adversarial attacks inherited from the pre-trained model
B. The functional performance of the pre-trained model could be lower than stakeholders expect
C. The functional performance of the system may exhibit bias
D. Differences in data preparation steps between the training of the pre-trained model and consequent use of the model may result in reduced functional performance

Answer: B

Explanation:
When using a pre-trained model, especially one that is highly accurate and widely used in industry, it is less likely that the functional performance will be lower than expected, given that it has already been proven effective in similar use cases.

NEW QUESTION # 33
"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 accuracy and recall
B. Maximize recall and precision
C. Maximize specificity number of classes
D. Maximize precision and accuracy

Answer: B

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 # 34
Which of the following statements about reinforcement learning is correct?
Choose ONE option (1 out of 4)

A. From experience, the agent learns theoptimal reward function
B. The agent creates a model of the environment from labeled data during training
C. The agent's training is based on a reward function that rewards successful attempts
D. The approach is suitable when the application doesnotrequire interaction with the environment

Answer: C

Explanation:
Section1.6.3 - Reinforcement Learningof the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on anagent interacting with an environment, performing actions, and receivingrewards or penalties.
The core concept is thereward function, which guides the agent's learning process. The syllabus emphasizes that training in RL isdriven by rewards, and the agent aims to maximize cumulative reward over time.
Therefore, OptionCdirectly reflects the correct description: the agent learns by being rewarded for successful actions .
Option A is incorrect because RL doesnotuse labeled data; that applies to supervised learning. Option B contradicts the syllabus definition: RL fundamentallyrequiresinteraction with the environment. Option D is incorrect because the reward function isdefined by humans, not learned by the agent; the agent learns apolicy, not the reward function itself.
Thus, OptionCis the only statement consistent with RL as defined in the syllabus.

NEW QUESTION # 35
Which of the following options is an example of the concept of overfitting?

A. A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.
B. A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.
C. A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
D. A model for predicting academic performance was trained with data from students at one university. The model shows low predictive accuracy when applied to other universities.

Answer: D

Explanation:
The ISTQB CT-AI syllabus defines overfitting in Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely--including noise and irrelevant detail-- resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. Option A perfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities.
This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .

NEW QUESTION # 36
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