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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 5	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 6	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 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	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	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 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	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 (Q67-Q72):

NEW QUESTION # 67
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

A. Explorability
B. Autonomy
C. Transparency
D. Accessibility

Answer: C

Explanation:
AI-based systems can sometimes behave like black boxes, where the internal decision-making process is unclear. Transparency refers to the ability to inspect and understand the training data, algorithms, and decision-making process of the AI system.
Transparency ensures that testers and stakeholders can review how an AI system was trained.
Access to training data is a key factor in transparency because it allows testers to analyze biases, completeness, and representativeness of the dataset.
Transparency is an essential characteristic of explainable AI (XAI). Having access to training data means that testers can investigate how data influences AI behavior.
Regulatory and ethical AI guidelines emphasize transparency. Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency to ensure fair and explainable AI decision-making.

NEW QUESTION # 68
Which ONE of the following statements about a system MOST describe an autonomous system?

A. A chatbot that learns the most effective responses to humans (in order to ensure the humans continue to converse) based on prior experiences.
B. A fraud detection system that alerts operators when it sees a specific fraud risk associated with a transaction, in order to obtain human input to make a final decision.
C. A self driving car that automatically stops the car if the driver is not responding to voice prompts, in order to ensure they are awake.
D. A loan approval system that can continue to process loan applications indefinitely, within a defined amount of aggregate credit, until an operator reduces the amount of aggregate credit available.

Answer: D

Explanation:
The correct answer is A because it best matches the CT-AI concept of autonomy: the system can operate independently for a prolonged period, processing loan applications within predefined constraints until a human changes the available aggregate credit. The syllabus defines autonomy as the ability of a system to work independently of human oversight and control for prolonged periods, and emphasizes that testers should identify how long the system is expected to perform satisfactorily without human intervention and when control should be returned to humans.
Option B describes a safety or driver-monitoring function rather than sustained independent operation; the system reacts when the driver is unresponsive but still assumes human supervision. Option C describes evolution or self-learning, because the chatbot improves responses based on prior interactions, but self- learning alone does not make the system autonomous. Option D is explicitly human-in-the-loop: the fraud system only alerts operators and requires human input for the final decision, so it is less autonomous.
References/topics: CT-AI Syllabus Chapter 2, Section 2.2 "Autonomy"; Appendix B "Autonomous system" and "Autonomy."

NEW QUESTION # 69
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. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
B. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
C. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
D. 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.

Answer: D

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 # 70
Which statement about using AI to analyze reported defects is MOST correct?

A. ML models can support duplicate defect identification when checking defect criticality.
B. ML models identify developers who should handle a defect based on ticket content.
C. ML models can identify categories for a reported defect during assignment.
D. ML models trained with critical defect tickets can identify defects that cause serious consequences.

Answer: C

Explanation:
The ISTQB CT-AI syllabus (Section5.3 - AI Support for Defect Analysis) explains that AI can categorize defect reports using natural language processing or classification models.
Categorization helps route defects efficiently and determine which areas of the system are affected. Thus, Option C is correct: AI canidentify defect categories, supporting assignment and triage.

NEW QUESTION # 71
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?

A. Test objective: Evidence that the data is free from inappropriate bias Acceptance criterion: The DPMA's analysis data is statistically compared to data from other sources.
B. Test objective: Evidence of functional safety Acceptance criterion: The system recognizes failures in the transmission of information and data with the DPMA system and the evaluation system by means of self-tests.
C. Test objective: Evidence of compatibility Acceptance criterion: The system can exchange information with the DPMA system and the evaluation system.
D. Test objective: Evidence of evolution Acceptance criterion: The quality of the research results does not deteriorate with further training.

Answer: D

Explanation:
The ISTQB CT-AI syllabus introduces AI-specific quality characteristics, including evolution, functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectives explains that evolution refers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
continuously reduce false positives,
achieve weekly accuracy improvements of 10%,
reach and maintain 90% accuracy,
adapt to new environments (patent law firm -> corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degradeas additional training data is introduced. Option B therefore directly supports the described acceptance criteria for this evolving, self-learning application.

NEW QUESTION # 72
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