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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	systems from those required for conventional systems.
Topic 5	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 6	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 7	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 8	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 9	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 (Q77-Q82):

NEW QUESTION # 77
Which statement regarding the use of training, validation, and test data sets is correct?
Choose ONE option (1 out of 4)

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

Answer: C

Explanation:
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that thetest 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.
Option A contradicts the syllabus because validation and test sets servedifferent purposes: validation is for tuning, test is for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test set or using techniques such as cross-validationrather than eliminating testing. Option C is wrong because equal distribution (33/33/33) isnot recommended; typically, the training set is much larger (e.g., 70-80%).
Thus, OptionDis the only statement aligned with the syllabus' guidance.

NEW QUESTION # 78
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?

A. High complexity
B. Self-learning
C. Non-determinism
D. Robustness

Answer: D

Explanation:
Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.

NEW QUESTION # 79
Which ONE of the below is MOST likely to indicate a problem with underfitting in an ML model?

A. The model is vulnerable to adversarial attacks
B. The model uses a large amount of resources to make a prediction
C. The model is inaccurate on data similar to the training data
D. The model fails to generalize on new data

Answer: C

Explanation:
Underfitting occurs when the model is too simplistic to capture the underlying patterns in the training data. This results in poor performance not only on new data but also on the data it was trained on. Thus, if the model is inaccurate on data similar to the training data, it suggests underfitting.

NEW QUESTION # 80
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?

A. Test for human handover after a given time interval.
B. Test for human handover when it should actually not be relinquishing control.
C. Test for human handover requiring mandatory relinquishing control.
D. Test for human handover to give rest to the system.

Answer: B

Explanation:
Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems.

NEW QUESTION # 81
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

A. All high priority defects will be identified using this method
B. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified
C. The number of parameters to test can be reduced to less than a dozen
D. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them

Answer: D

Explanation:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)

NEW QUESTION # 82
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