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

ISTQB ISTQB-CT-AI Online Questions & Answers

• Question 111:

  Which of the following is a dataset issue that can be resolved using pre-processing?

  A. Insufficient data
  B. Invalid data
  C. Wanted outliers
  D. Numbers stored as strings
  D. Numbers stored as strings

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  The ISTQB CT-AI syllabus explains that data pre-processing includes activities such as data cleaning and data transformation. Data transformation covers changes to the format or representation of data, for example converting data types or encodings so that they can be correctly processed by machine learning algorithms.

  Specifically, the syllabus states that transformation may include converting categorical values to numerical values or changing data representations, such as numbers stored as strings. This directly corresponds to Option D, which describes a dataset issue that can be resolved through pre-processing.

  Option A is incorrect because insufficient data is a data availability problem that cannot be solved by pre-processing. Option B is incorrect because invalid data is typically addressed through data cleaning rather than transformation. Option C is incorrect because wanted outliers are intentionally retained and should not be removed or altered during pre-processing.

  Thus, Option D is the correct answer.

• Question 112:

  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 113:

  Which data-labeling approach uses a two-step process where labeling is first done by a tool and then verified or completed by a human?

  A. Internal data labeling
  B. Crowdsourced data labeling
  C. Outsourced data labeling
  D. AI-assisted data labeling
  D. AI-assisted data labeling

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  Section 2.4 - Data Labeling Approaches of the ISTQB CT-AI syllabus explicitly defines AI-assisted data labeling as a hybrid approach in which an automated tool performs the initial labeling, and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while maintaining human oversight to ensure data quality. The syllabus presents this approach as an effective compromise when fully manual labeling would be too slow or costly and when automation

  can pre-label obvious patterns before human validation.

  Option A (internal data labeling) refers to labeling performed by the organization's own staff and does not inherently involve automation. Option B (crowdsourced data labeling) relies on a distributed group of human labelers, typically without an automated pre-labeling step. Option C (outsourced data labeling) delegates labeling tasks to an external provider but likewise does not imply an AI-first process. Only Option D reflects the automated-then-human workflow explicitly described in the syllabus.

  Therefore, AI-assisted data labeling (Option D) is the correct answer.

• Question 114:

  Which ONE of the following models BEST describes a way to model defect prediction by analyzing the history of bugs in modules, using code quality metrics of historical versions as input?

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

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  The ISTQB CT-AI syllabus explains that defect prediction commonly uses supervised learning classification models trained on historical defect data. In this approach, previous versions of software modules are labeled as defective or non- defective, and code quality metrics (such as complexity, size, or coupling) are used as input features.

  A classification model learns patterns from this historical data and predicts whether a given module is likely to contain defects. This directly matches Option D.

  The other options do not accurately describe this approach. Analyzing developer-module relationships does not focus on code metrics. Natural language processing of code similarity is not the standard technique for defect prediction.

  Clustering is an unsupervised technique and does not use labeled defect history in the way described.

  Therefore, Option D is the correct answer.

• Question 115:

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

  A. Inappropriate bias
  B. Automation bias
  C. Algorithmic bias
  D. Sample bias
  B. Automation bias

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  The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, including sample bias, algorithmic bias, and inappropriate bias. In Section 3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate from training data, model development, and decision logic. Sample bias occurs when the training data does not adequately represent the target 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 the outputs of the AI model and are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. Such biases can result in missed defects or false alarms, significantly impacting quality assurance.

  Automation bias, however, is fundamentally different. It refers to a human cognitive bias in which users (for example, inspectors) overly trust or rely on the AI system's output. While automation bias is important in user-interaction and human- AI collaboration testing, it is not a bias within the ML model itself. Since the question asks which bias is least important to look for when testing the model, automation bias can be deprioritized in model-level testing.

  Therefore, Option B is correct.

• Question 116:

  Which of the following technologies for implementing AI is considered to be a reasoning technique?

  A. Deductive classifiers
  B. Linear regression
  C. Random Forest
  D. Genetic algorithms
  A. Deductive classifiers

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  The ISTQB Certified Tester AI Testing Syllabus v1.0 explicitly categorizes different AI implementation technologies in Section 1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is "Reasoning techniques." These reasoning techniques include rule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are explicitly listed under this category, they are recognized as reasoning- based AI technologies.

  Reasoning techniques differ from machine learning approaches because they rely on structured, predefined rules or logic to reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge, rather than learning patterns statistically from data.

  The other options---linear regression, Random Forest, and genetic algorithms---are categorized in the syllabus as machine learning or optimization techniques, not reasoning techniques. Linear regression is a statistical prediction method, Random Forest is an ensemble-based machine learning model, and genetic algorithms are optimization approaches inspired by evolutionary processes.

  Thus, based on the syllabus definitions, Deductive classifiers (Option A) are the only technology considered to be a reasoning technique.

• Question 117:

  A neural network has been designed and created to assist day traders in improving efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network in which each neuron is examined. For this network, the shortest path indicates a "buy" decision, and this 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. Threshold coverage
  B. Neuron coverage
  C. Sign-change coverage
  D. Value-change coverage
  A. Threshold coverage

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  The ISTQB CT-AI syllabus defines threshold coverage as a structural coverage criterion for neural networks that requires each neuron to reach an activation value greater than a defined threshold during testing. The purpose is to ensure that neurons are sufficiently activated and that threshold-based decision logic is exercised.

  In this scenario, the testers explicitly verify that neurons activate only when the predicted future value exceeds the spot price by a specific threshold (0.75%). This directly matches the definition of threshold coverage, as the test checks neuron activation against a predefined activation threshold.

  The other options do not apply. Neuron coverage focuses on whether neurons are activated at all, not whether they exceed a specific threshold. Sign-change coverage and value-change coverage relate to changes in neuron activation signs or values, not to activation relative to a fixed threshold.

  Therefore, Option A is the correct answer.

• Question 118:

  Which of the following are challenges specifically related to testing AI-based systems? (Choose two)

  A. Non-deterministic behavior
  B. Requirement traceability
  C. Lack of explainability
  D. Syntax errors in source code
  A. Non-deterministic behavior
  C. Lack of explainability

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• Question 119:

  Which statement regarding flexibility and adaptability of AI-based systems is correct?

  A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.
  B. Adaptability is considered to be the ability of the system to be used in unspecified situations.
  C. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.
  D. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
  A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.

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  The ISTQB CT-AI syllabus defines flexibility and adaptability in Section 2.1 - Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situations not explicitly covered by its original requirements, whereas adaptability refers to how easily the system can be modified to handle new environments or operating conditions. The syllabus emphasizes that both properties are especially important for self-learning AI-based systems, which may need to adjust their behavior autonomously

  as conditions change.

  It further explains that such systems must be capable of determining when and how to adapt their behavior in evolving or previously unknown situations. This directly aligns with Option A, which highlights the importance of both adaptability and flexibility when a system needs to change its behavior and determine those changes on its own.

  Option B is incorrect because it reverses the definitions: operating in unspecified situations relates to flexibility, not adaptability. Option C is incorrect because self-learning AI-based systems are not classified as either adaptable-only or flexible-only; they typically require both characteristics. Option D is incorrect because ease of reprogramming corresponds to adaptability, not flexibility.

  Thus, Option A correctly reflects the syllabus definitions.

• Question 120:

  Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters. Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?

  A. Different Road Types
  B. Different weather conditions
  C. ML model metrics to evaluate the functional performance
  D. Different features like ADAS, Lane Change Assistance etc.
  C. ML model metrics to evaluate the functional performance

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  Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options is least likely to be a reason for the explosion in the number of parameters.

  Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.

  Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.

  ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.

  Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.

  Hence, the least likely reason for the incredible growth in the number of parameters is C. ML model metrics to evaluate the functional performance.