ISQI ISQI-CT-AI Online Practice Questions and Exam Preparation

ISQI ISQI-CT-AI Online Questions & Answers

• Question 71:

  Which ONE of the following BEST describes the AI Effect?

  A. The ability of AI systems to outperform humans in complex tasks
  B. The changing perception of what constitutes AI over time
  C. The use of probabilistic algorithms instead of deterministic ones
  D. The requirement that AI systems must be self-learning
  B. The changing perception of what constitutes AI over time

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• Question 72:

  Which ONE of the following statements correctly describes the importance of flexibility for AI systems?

  A. AI systems are inherently flexible.
  B. AI systems require changing operational environments; therefore, flexibility is required.
  C. Flexible AI systems allow for easier modification of the system as a whole.
  D. Self-learning systems are expected to deal with new situations without explicitly having to be programmed for them.
  C. Flexible AI systems allow for easier modification of the system as a whole.

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  The ISTQB CT-AI syllabus defines flexibility as the ease with which a system can be modified to accommodate changes, such as new requirements, constraints, or operating conditions. Flexibility supports maintainability and evolution of AI systems over time.

  Option C correctly captures this concept by stating that flexible AI systems allow for easier modification of the system as a whole.

  The other options are incorrect. AI systems are not inherently flexible; flexibility depends on design choices. The need to operate in changing environments does not by itself define flexibility. Dealing with new situations without explicit programming describes adaptability, especially in self-learning systems, not flexibility.

  Therefore, Option C is the correct answer.

• Question 73:

  Which option gives the correct values for accuracy and precision from the confusion matrix?

  The confusion matrix: True Positives (TP) = 15 False Positives (FP) = 5 False Negatives (FN) = 15 True Negatives (TN) = 65

  A. Accuracy = 50%, Precision = 75%
  B. Accuracy = 80%, Precision = 75%
  C. Accuracy = 75%, Precision = 80%
  D. Accuracy = 80%, Precision = 50%
  B. Accuracy = 80%, Precision = 75%

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  From the confusion matrix:

  True Positives (TP) = 15

  False Positives (FP) = 5

  False Negatives (FN) = 15

  True Negatives (TN) = 65

  Total number of instances = 100

  Accuracy is calculated as:

  (TP + TN) / Total

  = (15 + 65) / 100

  = 80%

  Precision is calculated as:

  TP / (TP + FP)

  = 15 / (15 + 5)

  = 15 / 20

  = 75%

  Section 3.2 - Functional Performance Criteria in the syllabus explains accuracy and precision using exactly these definitions when evaluating machine learning classification performance.

  Option B is therefore the correct answer.

• Question 74:

  Which of the following statements regarding experience-based testing for AI-based systems is correct?

  A. Intuitive test case design for AI-based systems involves interactive, hypothesis-driven examination of data for correlations or developmental trends.
  B. In checklist-based testing of AI-based systems, the existing test cases are dynamically adapted, for example based on metamorphic testing.
  C. Exploratory testing is often used for AI-based systems because there are often insufficient specifications or problems with the test oracle for AI-based systems.
  D. Tour refers to intuitive test case design for AI-based systems based on multiple, sequential test cases using systematically biased training data.
  C. Exploratory testing is often used for AI-based systems because there are often insufficient specifications or problems with the test oracle for AI-based systems.

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  The ISTQB CT-AI syllabus explains in Section 4.4 - Experience-Based Testing for AI Systems that AI-based systems frequently suffer from insufficient specifications, unpredictable model behavior, and test oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states that exploratory testing is especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, Option C accurately reflects the role and justification of exploratory testing for AI systems.

  Option A describes data analysis rather than intuitive test case design. Option B is incorrect because checklist-based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines tour-based testing; tours refer to structured exploratory approaches, not the use of biased training data. Therefore, Option C is the syllabus-aligned correct statement.

• Question 75:

  Which of the following neural network coverage criteria can be adapted for its application?

  A. Sign-Sign coverage
  B. Sign-Change coverage
  C. Threshold coverage
  D. Neuron coverage
  C. Threshold coverage

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  Section 4.2 - Test Coverage Criteria for AI Models of the ISTQB CT-AI syllabus describes neural network-specific coverage methods. Among these techniques, threshold coverage is explicitly noted as adaptable,meaning testers may choose different threshold values to determine whether neuron activation is considered "covered." This flexibility allows threshold coverage to be adjusted based on the model architecture, problem domain, and required level of test thoroughness.

  Options A and B (Sign-Sign coverage and Sign-Change coverage) are more rigid structural criteria and are not described as adaptable in the syllabus. They focus on sign patterns of neuron activations and do not support threshold adjustment. Option D (neuron coverage) measures whether a neuron has been activated at least once. Although simple, it is not defined as adaptable, and the syllabus notes that it often provides only shallow insight because high coverage is easily achieved.

  Only threshold coverage allows testers to adapt activation thresholds to refine coverage measurement. Therefore, Option C is the correct answer.

• Question 76:

  A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the automatic text correction feature. The testers have discovered that when they start typing "Isle of Wight," it is automatically corrected to "Isle of Eight." Several UAT testers have accepted this change without noticing it. What type of bias is this?

  A. Geographical/Locality
  B. Automation/Complacency
  C. Complacency/Disregard
  D. Ignorance/Cognitive
  B. Automation/Complacency

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  The ISTQB CT-AI syllabus defines automation bias as a cognitive bias in which people tend to favor or trust the outputs of an automated system over their own judgment or other information sources. This bias is also commonly referred to as complacency bias, where users accept automated decisions or suggestions without sufficient critical review. In this scenario, the automatic text correction system produces an incorrect suggestion ("Isle of Eight" instead of "Isle of Wight"), and several UAT testers accept this output without noticing the error. This behavior directly reflects automation/ complacency bias, as the testers rely on the automated system's output rather than verifying its correctness. Therefore, Option B correctly identifies the type of bias described.

• Question 77:

  Which of the following statements about ML functional performance metrics is correct?

  A. Metrics used to measure clustering include intra-cluster metrics that measure the proximity of a cluster's data points.
  B. The R-squared metric indicates how well the model distinguishes between different classes based on the ROC curve.
  C. The silhouette coefficient describes how well the regression model fits the dependent variables.
  D. The receiver operating characteristic curve shows, depending on parameters, how well the model distinguishes between different clusters.
  A. Metrics used to measure clustering include intra-cluster metrics that measure the proximity of a cluster's data points.

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  The ISTQB CT-AI syllabus explains machine learning performance metrics in Section 3.2 - Evaluating ML Models. For clustering, which is an unsupervised learning approach, the syllabus lists metrics such as intra-cluster distance, inter- cluster distance, and cohesion measures. Intra-cluster metrics evaluate how close data points are within the same cluster, which directly corresponds to Option A.

  Option B is incorrect because R-squared is a regression metric used to measure goodness of fit and is not related to classification performance or ROC curves. Option C is incorrect because the silhouette coefficient is a clustering metric that measures cohesion and separation between clusters, not regression model fit. Option D is incorrect because receiver operating characteristic curves are used to evaluate binary or multiclass classification models, not clustering models.

  Thus, Option A is the only statement that accurately reflects the syllabus.

• Question 78:

  There is a growing backlog of unresolved defects in your project. You know the developers have created an ML model that has learned which developers work on which types of software and the speed at which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

  A. Use it to prioritize defects automatically based on the expected time for the fix, the speed of the fix, and the likelihood of regressions
  B. Use it to assign defects to the most suitable developer to resolve the problem and to balance the defect workload among developers
  C. Use it to determine the root cause of each defect and develop a process improvement plan to eliminate the most common causes
  D. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas
  B. Use it to assign defects to the most suitable developer to resolve the problem and to balance the defect workload among developers

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  The ISTQB CT-AI syllabus explains that machine learning models can support defect analysis and defect management by learning from historical defect data. One specific application is defect assignment, where ML models analyze defect descriptions, past assignments, and resolution performance to recommend the most appropriate developer to fix a defect. This can also help balance workloads across the development team, improving overall efficiency and reducing backlog.

  This directly corresponds to Option B.

  The other options describe different AI-supported activities. Automatic prioritization based on regression likelihood is not the primary use case described for such models. Root cause analysis and process improvement require additional analysis beyond assignment prediction. Predicting defect-prone code areas relates to defect prediction, not defect assignment.

  Therefore, Option B is the correct answer.

• Question 79:

  Which ONE of the following options does NOT describe an AI-technology-related characteristic that differentiates AI test environments from other test environments?

  A. Challenges resulting from low accuracy of the models.
  B. The challenge of mimicking undefined scenarios generated due to self-learning.
  C. The challenge of providing explainability for the decisions made by the system.
  D. Challenges in the creation of scenarios for human handover in autonomous systems.
  D. Challenges in the creation of scenarios for human handover in autonomous systems.

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  The ISTQB CT-AI syllabus explains that AI test environments differ from traditional test environments mainly due to technology-related characteristics of AI systems. These include model accuracy limitations, the emergence of undefined or unexpected scenarios caused by self-learning behavior, and the need to provide explainability for AI decisions. Options A, B, and C all directly reflect such AI-specific, technology-driven challenges.

  Option D, however, concerns the creation of human handover scenarios in autonomous systems. While this is an important testing concern, it is primarily related to operational, human-machine interaction, and deployment aspects, rather than being an intrinsic AI-technology-related characteristic that differentiates AI test environments themselves.

  Therefore, Option D is the correct answer.

• Question 80:

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

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

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  The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:

  Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.

  Robustness (B): 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.

  High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.

  Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.

  ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.