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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3	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 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	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	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.

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

NEW QUESTION # 12
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?

A. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
B. Clustering of similar code modules to predict based on similarity.
C. Identifying the relationship between developers and the modules developed by them.
D. Search of similar code based on natural language processing.

Answer: A

Explanation:
Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.

NEW QUESTION # 13
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

A. They are slow and will usually not be able to execute in the time allowed
B. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures
C. They are defect-prone because they are unable to detect nuances in the requirements
D. They make debugging more complicated because the number of steps is usually high in order to induce the target failure

Answer: B

Explanation:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)

NEW QUESTION # 14
Which ONE of the following statements is MOST true about black-box adversarial testing?

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

Answer: A

Explanation:
In black-box adversarial testing, the tester does not have access to the internal workings of the model (such as the algorithm or the training data). The approach relies on the transferability of the attacks, where adversarial examples generated for one model are used to test the robustness of another model, even without direct access to its internal details.

NEW QUESTION # 15
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the "from" portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?

A. EDA cannot be used to detect the attack
B. EDA can help detect the outlier emails from the real emails
C. EDA can restrict how many inputs can be provided by unique users
D. EDA can detect and remove the false emails

Answer: B

Explanation:
The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning:
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.1.2, page 67 of 99)

NEW QUESTION # 16
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)

A. 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.
B. Test objective: Evidence of evolution Acceptance criterion: The quality of the research results does not deteriorate with further training.
C. 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.
D. Test objective: Evidence of compatibility Acceptance criterion: The system can exchange information with the DPMA system and the evaluation system.

Answer: B

Explanation:
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains thatevolutionrefers 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. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.

NEW QUESTION # 17
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