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

ISQI ISQI-CT-AI Online Questions & Answers

• Question 1:

  Which of the following statements about reinforcement learning is correct?

  A. The agent creates a model of the environment from labeled data during training.
  B. The approach is suitable when the application does not require interaction with the environment.
  C. The agent's training is based on a reward function that rewards successful attempts.
  D. From experience, the agent learns the optimal reward function.
  C. The agent's training is based on a reward function that rewards successful attempts.

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  explanation:

  Section 1.6.3 - Reinforcement Learning of the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on an agent interacting with an environment, performing actions, and receiving rewards or penalties. The core concept is the reward function, which guides the agent's learning process. The syllabus emphasizes that training in RL is driven by rewards, and the agent aims to maximize cumulative reward over time. Therefore, Option C correctly describes reinforcement learning: the agent's

  training is based on a reward function that rewards successful actions.

  Option A is incorrect because reinforcement learning does not rely on labeled data; that is characteristic of supervised learning. Option B is incorrect because reinforcement learning fundamentally requires interaction with the environment. Option D is incorrect because the reward function is defined externally (for example, by designers or domain experts); the agent learns a policy to maximize reward, not the reward function itself.

  Thus, Option C is the only statement consistent with reinforcement learning as defined in the syllabus.

• Question 2:

  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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  explanation:

  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 3:

  Which statement regarding testing transparency, explainability, or interpretability is MOST correct?

  A. Tests for explainability and transparency are comparable to exploratory testing and can be performed with little information about development.
  B. Since different users have different backgrounds, interpretability testing depends on the comprehensibility of the ML algorithm.
  C. Dynamic testing is one way to quantify explainability; however, each method is specific to a particular model type.
  D. LIME can precisely state the decisive reason for a change in the output.
  B. Since different users have different backgrounds, interpretability testing depends on the comprehensibility of the ML algorithm.

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  explanation:

  The ISTQB CT-AI syllabus states in Section 2.10 - Explainability, Transparency, and Interpretability that interpretability is user-dependent, meaning that different users may understand explanations in different ways. This is because interpretability depends not only on the machine learning algorithm but also on the user's domain knowledge, experience, and expectations. Option B directly reflects this syllabus principle: interpretability testing must take user background into account, and explanations must be comprehensible to the intended user group.

  Option A is incorrect because testing for explainability and transparency typically requires substantial information about the model, data, and expected behavior, and is not comparable to exploratory testing performed with little prior knowledge. Option C is incorrect because explainability is not generally quantifiable through dynamic testing alone, and the syllabus does not claim that explainability methods are strictly tied to specific model types in this manner. Option D is incorrect because LIME provides approximate, local explanations and cannot precisely identify the decisive root cause of output changes; the syllabus emphasizes that such explanations are approximations with inherent limitations.

  Therefore, Option B is the most syllabus-aligned and correct statement.

• Question 4:

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

  A. 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.
  B. Test objective: Evidence of evolution Acceptance criterion: The quality of the research results does not deteriorate with further training.
  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.
  B. Test objective: Evidence of evolution Acceptance criterion: The quality of the research results does not deteriorate with further training.

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  explanation:

  The ISTQB CT-AI syllabus introduces AI-specific quality characteristics, including evolution, functional safety, compatibility, and bias-related data quality. Section 5.1 - AI-Specific Test Objectives explains that evolution refers to an AI system's capability to continue improving or at least maintain its performance as it undergoes additional training.

  GPT_Legal is explicitly described as a self-learning system expected to continuously reduce false positives, achieve weekly accuracy improvements of 10%, reach and maintain 90% accuracy, and adapt to new environments such as a patent law firm or a corporate legal department. This aligns directly with the syllabus definition of evidence of evolution, which focuses on ensuring that model quality does not degrade as new training data is introduced. Option B therefore best matches both the system

  characteristics and the acceptance criterion.

  Option A is not appropriate because functional safety is relevant for safety-critical domains, which do not apply to patent search and legal drafting. Option C addresses compatibility, which is important but not the primary AI-specific objective in this scenario. Option D relates to bias, which is a relevant concern but not central to the described goals of continuous learning and performance improvement.

  Thus, Option B is the most appropriate AI-specific test objective and acceptance criterion.

• Question 5:

  Which of the following describes the AI effect?

  A. The changing perception of what constitutes AI
  B. The ability of AI to learn from data itself
  C. The fact that mankind can build intelligent machines
  D. The ability of AI to defeat a human, for example in chess
  A. The changing perception of what constitutes AI

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  explanation:

  The AI Effect is clearly defined in the ISTQB Certified Tester AI Testing Syllabus v1.0 under Section 1.1 - Definition of AI and AI Effect. The syllabus explains that society's perception of what qualifies as "AI" changes over time. Technologies that were once considered artificial intelligence---such as expert systems from the 1970s and 1980s or early chess-playing systems---are no longer viewed as AI once they become well understood or widely adopted. This phenomenon is explicitly referred to as the AI

  Effect and is described as the changing perception of what constitutes AI.

  Option B is incorrect because the ability to learn from data is a characteristic of machine learning, not a description of the AI Effect. Option C refers to the general capability of building intelligent machines and does not address the perceptual shift captured by the AI Effect. Option D describes a historical achievement in AI, such as defeating a human in chess, which the syllabus explicitly cites as an example of technology that is no longer considered AI due to the AI Effect.

  Therefore, Option A accurately reflects the AI Effect as defined in the ISTQB CT-AI syllabus.

• Question 6:

  How can a tester check the system for bias as part of a review of data sources, acquisition, and preprocessing?

  A. During the review, it can uncover algorithmic bias by analysing the procedures used to obtain the training data.
  B. During the review of the preprocessing, the auditor can uncover whether the data has been influenced in a way that could lead to sample distortions.
  C. It may use the LIME method as part of its data collection review to detect inappropriate bias.
  D. As part of the review of preprocessing, it can reveal whether the data has been influenced in a way that could lead to algorithmic bias.
  B. During the review of the preprocessing, the auditor can uncover whether the data has been influenced in a way that could lead to sample distortions.

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  explanation:

  Bias detection at the data level is performed by reviewing data acquisition and preprocessing steps, as explained in Section 2.3 - Data Quality and Bias of the ISTQB CT-AI syllabus. Sample bias can arise when data is distorted or when preprocessing introduces unintended shifts, for example through filtering, normalization, or labeling steps that disproportionately affect certain subsets of the data. Option B correctly reflects this approach: during preprocessing review, testers can identify whether the data has

  been influenced in a way that could lead to sample distortions.

  Option A is incorrect because algorithmic bias originates from the model and its learning process, not directly from data acquisition procedures. Option C is incorrect because LIME is an explainability technique applied to trained models, not a method used during data source or preprocessing reviews. Option D is incorrect because preprocessing affects sample bias rather than algorithmic bias.

  Thus, Option B correctly matches the syllabus guidance on detecting bias during reviews of data sources, acquisition, and preprocessing.

• Question 7:

  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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  explanation:

  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 8:

  Which statement regarding AI for defect prediction is correct?

  A. AI-based defect prediction is most effective when based on previous similar constellations.
  B. AI-based defect prediction can detect whether defects exist but not where.
  C. AI-based defect prediction is most effective when based on source-code metrics such as branches or McCabe complexity.
  D. AI-based defect prediction is based on formal principles and requires only a few factors.
  A. AI-based defect prediction is most effective when based on previous similar constellations.

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  explanation:

  Section 5.3 - AI Support for Defect Prediction of the ISTQB CT-AI syllabus explains that AI-based defect prediction relies on identifying historical patterns, including past defect data, code behavior, and similar system configurations. Machine learning models trained on historical defect information can estimate which components are more likely to contain defects when new changes resemble previously defect-prone constellations. This directly supports Option A, which states that defect prediction is most effective when based on previous similar constellations.

  Option B is incorrect because AI-based defect prediction can indicate where defects are likely to occur, for example at the component or module level, not only whether defects exist. Option C is incomplete because, although source-code metrics such as complexity or branching contribute useful information, defect prediction typically depends on a combination of factors, including historical defects, code churn, change frequency, and contextual data. Option D is incorrect because defect prediction is not based on formal principles alone and usually requires many input features rather than only a few.

  Thus, Option A is the correct and syllabus-consistent answer.

• Question 9:

  Which statement about AI-based test case generation is correct?

  A. AI-generated functional test cases typically result in poor requirements coverage.
  B. A different test oracle is usually required for each AI-generated functional test case.
  C. Expected results may not be available for AI-generated functional test cases.
  D. An AI-based system under test must not be used as a functional test oracle.
  C. Expected results may not be available for AI-generated functional test cases.

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  explanation:

  The ISTQB CT-AI syllabus indicates in Section 5.2 - AI for Testing that AI-generated test cases may not come with predefined expected results. This is because AI-based test case generation techniques---such as evolutionary algorithms, reinforcement learning, or clustering-based sampling---primarily generate input data. Determining the correct expected output often still requires human judgment or additional mechanisms. Therefore, Option C is correct: expected results may not be available, especially

  when AI generates novel or previously unseen input combinations.

  Option A is incorrect because AI-based test case generation can improve requirements and input-space coverage by exploring large and complex input domains. Option B is incorrect because the need for test oracles does not inherently depend on whether test cases are generated by AI. Option D is incorrect because the syllabus allows the use of an AI-based system as a functional oracle in back-to-back testing scenarios, provided that the limitations of this approach are understood.

  Therefore, Option C is the correct syllabus-aligned statement.

• Question 10:

  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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  explanation:

  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.