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

ISQI ISQI-CT-AI Online Questions & Answers

• Question 101:

  An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases. Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

  A. Labeling the data correctly
  B. Minimizing the amount of time spent training the algorithm
  C. Selecting the correct data pipeline for the ML training
  D. Grouping similar products together before feeding them into the algorithm
  A. Labeling the data correctly

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  The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:

  "In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."

  (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

• Question 102:

  Which ONE of the following BEST describes sample bias?

  A. Bias introduced by incorrect hyperparameter values
  B. Bias caused by the algorithm's decision logic
  C. Bias resulting from non-representative training data
  D. Bias introduced by explainability techniques
  C. Bias resulting from non-representative training data

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• Question 103:

  "Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has a 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients. Which ONE of the following combinations requires MAXIMIZATION?

  A. Maximize precision and accuracy
  B. Maximize accuracy and recall
  C. Maximize recall and precision
  D. Maximize specificity and number of classes
  C. Maximize recall and precision

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  The low prevalence rate (2%) indicates a highly imbalanced dataset, where cancer cases represent a very small minority of the population. In such scenarios, accuracy alone is misleading, as a model could achieve high accuracy by predominantly predicting the majority class (normal patients).

  Recall (sensitivity) is critical in medical diagnosis because it measures the proportion of actual cancer cases correctly identified. Missing cancer cases (false negatives) can have severe consequences, so recall must be maximized.

  Precision is also important because it measures how many predicted cancer cases are truly cancer cases.

  High precision reduces false positives, avoiding unnecessary anxiety and invasive follow-up procedures for healthy patients.

  When both false negatives and false positives have significant impact, as in cancer detection, the ISTQB CT-AI syllabus emphasizes the need to maximize both recall and precision, rather than relying on accuracy or specificity alone.

  Therefore, Option C (Maximize recall and precision) is the correct answer.

• Question 104:

  Which of the following test techniques are commonly used for AI systems when no clear test oracle exists? (Choose two)

  A. Metamorphic testing
  B. Back-to-back testing
  C. Boundary value analysis
  D. Exploratory testing
  A. Metamorphic testing
  D. Exploratory testing

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• Question 105:

  Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

  

  For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?

  A. 0.87, 0.9, 0.84
  B. 1, 0.87, 0.84
  C. 1, 0.9, 0.8
  D. 0.84, 1, 0.9
  A. 0.87, 0.9, 0.84

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  To calculate accuracy, recall, and specificity from the confusion matrix, the following values are used: Confusion Matrix Actually Rotten: True Positive (TP) = 45 False Positive (FP) = 8 Actually Fresh: False Negative (FN) = 5 True Negative (TN) = 42 Accuracy Accuracy is the proportion of all correct predictions. Formula:

  Accuracy = (TP + TN) / (TP + TN + FP + FN)

  Calculation:

  Accuracy = (45 + 42) / (45 + 42 + 8 + 5)

  Accuracy = 87 / 100

  Accuracy = 0.87

  Recall (Sensitivity)

  Recall is the proportion of actual positives correctly identified.

  Formula:

  Recall = TP / (TP + FN)

  Calculation:

  Recall = 45 / (45 + 5)

  Recall = 45 / 50

  Recall = 0.9

  Specificity

  Specificity is the proportion of actual negatives correctly identified.

  Formula:

  Specificity = TN / (TN + FP)

  Calculation:

  Specificity = 42 / (42 + 8)

  Specificity = 42 / 50

  Specificity = 0.84

  Therefore, the correct combination of accuracy, recall, and specificity is:

  0.87, 0.9, 0.84

  Reference

  ISTQB CT-AI Syllabus v1.0, Section 5.1 - Confusion Matrix and ML Functional Performance Metrics.

• Question 106:

  Which ONE of the following is the MOST appropriate reason to use black-box testing for an AI-based system?

  A. The tester wants to verify the internal weights of the model
  B. The internal structure is too complex to understand and provides little test value
  C. The system must comply with coding standards
  D. The system output is deterministic
  B. The internal structure is too complex to understand and provides little test value

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• Question 107:

  Which assignment of AI techniques to testing support is BEST?

  A. Classification for the optimization of regression test cases
  B. Probabilistic methods for the prediction of system failures
  C. Fuzzy logic for the generation of test cases
  D. Computational optimization techniques for defect prediction
  B. Probabilistic methods for the prediction of system failures

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  The ISTQB CT-AI syllabus (Section 5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods - one of the three major AI technique groups --- are used to predict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with Option B.

  Option A is incorrect because regression test optimization is typically performed using search-based or optimization techniques rather than classification. Option C is incorrect because fuzzy logic is mainly suited to reasoning under vagueness and imprecision, not to the generation of test cases. Option D is incorrect because defect prediction commonly relies on statistical learning or classification models rather than computational optimization techniques.

  Thus, Option B is the most syllabus-consistent mapping of AI techniques to testing tasks.

• Question 108:

  Which of the following is the LEAST appropriate test to be performed when testing a feature related to autonomy?

  A. Test for human handover to give rest to the system.
  B. Test for human handover when the system should actually not be relinquishing control.
  C. Test for human handover requiring mandatory relinquishing of control.
  D. Test for human handover after a given time interval.
  B. Test for human handover when the system should actually not be relinquishing control.

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  The ISTQB CT-AI syllabus explains that testing autonomous AI-based systems includes verifying correct handover behavior between the system and human operators. This involves ensuring that handover occurs when required (for safety, rest, legal, or operational reasons) and that the system behaves correctly during and after the transition.

  Tests such as mandatory handover, time-based handover, or handover to allow the system to rest or reset are all valid and relevant scenarios when assessing autonomy and human-AI interaction.

  Option B, however, describes testing for a handover in a situation where the system should not relinquish control. This contradicts the intended autonomous behavior and does not represent a meaningful or appropriate test objective for validating autonomy features. Therefore, it is the least appropriate test.

  Thus, Option B is the correct answer.

• Question 109:

  Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?

  A. Generating test cases for the system before and after the change, since neither has a test oracle.
  B. Comparing outputs before and after the change using different inputs.
  C. Comparing outputs before and after the change using identical inputs.
  D. Comparing outputs of a non-self-learning system with those of the changed self-learning system.
  C. Comparing outputs before and after the change using identical inputs.

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  According to Section 4.6 --- AI Behaviour Testing (AIB Testing) of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle is comparing pre-change and post- change model behavior using the same test inputs so that any differences in outputs can be attributed to the model's learning rather than to changes in the input data. This directly corresponds to Option C.

  Option A is incorrect because the absence of a test oracle does not justify generating new test cases; AIB testing relies on reusing identical inputs to detect behavioral drift. Option B is incorrect because using different inputs prevents meaningful comparison of behavior changes. Option D is incorrect because comparing a self-learning system with an unrelated non-self-learning system does not allow evaluation of how the same model's behavior has evolved over time.

  Thus, Option C accurately represents the correct application of AIB testing.

• Question 110:

  Consider an AI-based system in which the complex internal structure has been generated by another software system. Why would a tester choose to perform black-box testing on this particular system?

  A. Test automation can be built quickly and easily from the test cases developed during black-box testing.
  B. The tester wishes to better understand the logic of the software used to create the internal structure.
  C. Black-box testing allows the tester to check the transparency of the algorithm used to create the internal structure.
  D. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-based system.
  D. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-based system.

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  The ISTQB CT-AI syllabus explains that many AI-based systems have internal structures that are highly complex, automatically generated, or not interpretable by humans. In such cases, understanding the internal structure does not provide additional useful information for testing.

  The syllabus states that when the internal workings of an AI-based system are too complex or opaque, testing must be performed using a black-box approach, focusing on inputs and outputs rather than internal logic. Therefore, black-box testing is chosen specifically because the tester does not need to understand the internal structure of the AI-based system.

  Options A, B, and C are incorrect because black-box testing is not intended to simplify automation creation, reveal internal logic, or assess transparency of the algorithm itself.

  Thus, Option D correctly reflects the rationale for using black-box testing in this context.