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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3	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 4	systems from those required for conventional systems.
Topic 5	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 6	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 7	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 8	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 (Q141-Q146):

NEW QUESTION # 141
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?

A. Compliance needs require proper care to be taken of input personal data.
B. Nature of data constantly changes with lime.
C. Data for the use case is being generated at a fast pace.
D. Test data being sourced from public sources.

Answer: C

Explanation:
Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.

NEW QUESTION # 142
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least
0.75%. Which of the following statements BEST explains the type of coverage being tested on the neural network?

A. Neuron coverage
B. Threshold coverage
C. Sign-change coverage
D. Value-change coverage

Answer: B

Explanation:
The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:
"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold."

NEW QUESTION # 143
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?

A. Test objective: Evidence of evolution Acceptance criterion: The quality of the research results does not deteriorate with further training.
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 that the data is free from inappropriate bias Acceptance criterion: The DPMA's analysis data is statistically compared to data from other sources.

Answer: A

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 # 144
Which of the following decisions is BEST as a test approach for the described situation?

A. You plan to perform reviews and exploratory data analysis of the image data sets to reduce the risk of a lack of representativeness of this data.
B. You execute the test cases from the old camera model at the integration test level; no further dynamic tests of the operating data pipeline are necessary.
C. You plan to manually execute regular regression tests of the new camera function, particularly for system tests.
D. You plan experience-based testing by the entire team at the system test level to ensure that the end users are satisfied.

Answer: D

Explanation:
The ISTQB CT-AI syllabus emphasizes that testing AI-based systems require scross-functional collaboration and experience-based testingwhen parts of the team lack domain knowledge. In this scenario, the ML expert understands ML and dataset preparation but lacks knowledge ofcamera system behavior, the device's operational data pipeline, and end-user workflows. The remainder of the team understands the domain and system testing but not ML. Section4.4 - Human Factors and AI Testingand4.3 - System Testing of AI Components highlight that when domain understanding is unevenly distributed,experience-based testing conducted by the full team(testers, developers, domain experts) is the most effective approach. This ensures that AI outputs align with actual user expectations and system behavior. Option C aligns exactly with this principle.

NEW QUESTION # 145
Which statement regarding the use of training, validation, and test data sets is correct?

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

Answer: B

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 # 146
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