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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	systems from those required for conventional systems.
Topic 2	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 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4	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 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7	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 8	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 9	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 10	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 11	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Choose ONE option (1 out of 4)

A. Robustness
B. Determinism
C. Interpretability
D. Complexity

Answer: D

Explanation:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:
complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness. Among these,complexityis a core challenge because modern AI systems-particularly those using deep learning-have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
Option A (Determinism) is the opposite of AI behavior; AI is oftennon-deterministic, and determinism does notmake systems unsafe. Option C (Interpretability) does impact trust and explainability, but the syllabus positions it as a transparency challenge, not the primary difficulty in ensuring safety. Option D (Robustness) is a desired quality, not a reason safety is hard; alackof robustness would be a challenge, not robustness itself.
Thus,complexitybest reflects the syllabus' explicit safety-related difficulty.

NEW QUESTION # 48
Which ONE of the following statements is MOST true about black box adversarial testing?

A. Combinatorial testing techniques are used
B. The algorithm used for the model is known to the tester
C. The training data is manipulated
D. The approach relies on the transferability of the attacks

Answer: D

Explanation:
The correct answer is B . The CT-AI syllabus explains that black-box adversarial attacks involve exploring the model to determine its functionality and then building a duplicate model with similar functionality. The attacker then uses a white-box approach on the duplicate model to identify adversarial examples. Because adversarial examples are generally transferable, those examples will normally also work on the original model.
Option A describes white-box adversarial testing, where the algorithm, model settings, and parameters are known. Option C describes data poisoning , where training data is manipulated to induce incorrect future predictions or create vulnerabilities. Option D describes pairwise or broader combinatorial testing, which is used where many parameters and values make exhaustive testing impractical. Black-box adversarial testing is instead based on limited knowledge of the target model and the empirical transferability of adversarial examples. If a duplicate model cannot be built, the syllabus also notes that high-volume automated testing may be used to discover adversarial examples and observe results.
References/topics: CT-AI Syllabus Chapter 9, Section 9.1.1 "Adversarial Attacks"; Section 9.1.2 "Data Poisoning."
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NEW QUESTION # 49
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

A. Testing the accuracy of the classification model.
B. Testing the speed of the prediction by the model.
C. Testing the API of the service powered by the ML model.
D. Testing the speed of the training of the model.

Answer: D

Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

NEW QUESTION # 50
Which statement regarding the use of training, validation, and test data sets is correct?

A. If limited data is available, it may be better to work without a separate test data set.
B. If only limited data is available, validation and test data sets can be combined in multiple ways during training.
C. 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.
D. Optimally, the data should be distributed equally between the training, validation, and test data sets.

Answer: C

Explanation:
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that the test dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.

NEW QUESTION # 51
Which TWO of the below examples of systems BEST describe regression?
i. Predicting the age of a person
ii. Predicting if someone is aged over 18
iii. Predicting how much fuel is required for a journey
iv. Predicting whether a release will pass all required tests

A. I, II
B. I, Iv
C. I, II
D. II, In

Answer: A

Explanation:
The technically correct pair is i and iii . The option list appears to contain a transcription duplication; D is selected as the intended option for I and III . Regression is used when the ML model predicts a numeric or continuous output value . The CT-AI syllabus explicitly gives "predicting the age of a person" as an example of regression, because age is a numeric output. It also explains that regression is part of supervised learning where the required output is numeric rather than a predefined category.
Example iii, predicting how much fuel is required for a journey, is also regression because the expected result is a numeric quantity, such as litres, gallons, or kilowatt-hours. Example ii is classification because the result is a category: over 18 or not over 18. Example iv is also classification because the output is pass or fail. The key distinction is that regression predicts a numeric value, while classification assigns inputs to discrete predefined classes.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.1 "Supervised Learning"; Appendix B
"Regression."
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NEW QUESTION # 52
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