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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4
  • systems from those required for conventional systems.
Topic 5
  • 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 6
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
The training of an ML model... What type of bias is LEAST important to look for when testing the model?

Answer: B

Explanation:
The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, includingsample bias,algorithmic bias, andinappropriate bias. In Section3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate fromtraining data,model development, anddecision logic. Sample bias occurs when the training data does not adequately represent the population; algorithmic bias arises when the model produces systematically skewed results due to learned patterns; inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect theoutputs of the AI modeland are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. These biases can lead to defective items being missed or false alarms being raised, which impacts quality assurance significantly .
Automation bias, however, is fundamentally different. It refers to ahuman cognitive bias, where users (e.g., inspectors) overly trust or rely on the AI system's output. While important in user- interaction testing, it isnota biaswithin the ML model itself. Since the question asks which bias isleast important when testing the model, automation bias can be legitimately deprioritized duringmodel-level testing. Therefore, Option B is correct.


NEW QUESTION # 17
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labeling approaches can be used?

Answer: C

Explanation:
Annotation is the correct labeling approach for supervised learning, as it involves manually labeling the images with the correct information, such as marking the location of the football in the image. This labeled data can then be used to train a machine learning model.


NEW QUESTION # 18
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?

Answer: B

Explanation:
Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.
* Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.
* Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.
* Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
* (B) Neuron Coverage#
* Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.
* (C) Sign-Change Coverage#
* This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).
* (D) Value-Change Coverage#
* This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.
* Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation." Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).


NEW QUESTION # 19
Which assignment of AI techniques to testing support is BEST?

Answer: C

Explanation:
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods--one of the three major AI technique groups--are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with Option B.


NEW QUESTION # 20
Which two test procedures are BEST suited for CleverPropose system testing?
Choose TWO options (2 out of 5)

Answer: B,E

Explanation:
The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to- back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.
Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.
Thus,A and Care the correct and syllabus-supported choices.


NEW QUESTION # 21
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