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

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
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 # 13
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting training data by purposely including all known faulty conditions
B. Selecting training data by purposely excluding specific faulty conditions
C. Selecting testing data from a boat manufacturer's bolt longevity data
D. Selecting testing data from a different dataset than the training dataset

Answer: B

Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately."

NEW QUESTION # 14
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?

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

Answer: B

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 # 15
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

A. Clustering is classification of a continuous quantity.
B. Clustering is supervised learning.
C. Clustering is done without prior knowledge of output classes.
D. Clustering requires you to know the classes.

Answer: C

Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.

NEW QUESTION # 16
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

A. 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.
B. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
C. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
D. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

Answer: A

Explanation:
* A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
* B. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
* Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
* C. 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.
* This approach directly compares the performance of two implementations of the same algorithm.
If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
* D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, optionCis the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.

NEW QUESTION # 17
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The modern ISTQB world is changing its dynamics at a fast pace. With the ISTQB CT-AI certification, you can learn these changes and stay updated all the time. There are other countless Certified Tester AI Testing Exam (CT-AI) certification exam benefits that you can gain after passing the exam. The prominent Certified Tester AI Testing Exam (CT-AI) certification exam benefits are validation of skills, more career opportunity, salary increment, and the opportunity to become a member of the ISTQB community.

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