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

ISQI ISQI-CT-AI Online Questions & Answers

• Question 51:

  Which statement about testing levels for AI-based systems is correct?

  A. Input data testing checks whether the inputs from the data pipeline are received by the model correctly and exchanged with all system components.
  B. Acceptance testing checks non-functional requirements such as explainability.
  C. ML model testing ensures that the relevant ML functional performance criteria are met.
  D. If AI is offered as a service, system testing includes API tests of the service.
  C. ML model testing ensures that the relevant ML functional performance criteria are met.

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  Section 4.3 - Test Levels for AI Systems clearly defines ML model testing as the level at which testers evaluate whether a machine learning model fulfills its functional performance criteria, including accuracy, precision, recall, F1-score, robustness, stability, and fairness. Therefore, Option C is the correct and syllabus-aligned statement.

  Option A is incorrect because input data testing focuses on the validity and correctness of data entering the model, not on interactions with all system components. Option B is incorrect because acceptance testing in the syllabus focuses primarily on business and stakeholder requirements, not specifically on explainability; explainability testing may occur at multiple test levels depending on context. Option D is incorrect because API testing belongs to integration testing rather than system testing, even when

  AI functionality is consumed as a service.

  Thus, Option C is the only statement that precisely matches the syllabus definitions.

• Question 52:

  In a conference on artificial intelligence (AI), a speaker made the statement:

  "The current implementation of AI using models which do NOT change by themselves is NOT true AI."

  Based on your understanding of AI, is this statement CORRECT or INCORRECT, and why?

  A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.
  B. This statement is correct. In general, what is considered AI today may change over time.
  C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.
  D. This statement is correct. In general, today the term AI is utilized incorrectly.
  B. This statement is correct. In general, what is considered AI today may change over time.

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  The ISTQB CT-AI syllabus describes the AI Effect, which explains that the perception of what qualifies as "AI" changes over time. Technologies that were once considered artificial intelligence may later be viewed as standard or conventional computing once they become widely understood and commonly used.

  Therefore, the statement is correct in the sense that what is considered AI today may not be considered AI in the future. The definition and perception of AI evolve as technology advances and societal expectations change.

  Option B correctly reflects this principle.

• Question 53:

  Which ONE of the following options describes a scenario of A/B testing the LEAST?

  A. A comparison of two different websites for the same company to observe user acceptance.
  B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users.
  C. A comparison of the performance of an ML system on two different input datasets.
  D. A comparison of the performance of two different ML implementations on the same input data.
  C. A comparison of the performance of an ML system on two different input datasets.

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  The ISTQB CT-AI syllabus describes A/B testing (also known as split testing) as a technique used to compare two variants of a system, feature, or model under the same conditions, in order to determine which performs better according to defined metrics.

  Options A, B, and D all align with this definition. They involve comparing two alternatives (websites, offers, or ML implementations) while keeping the conditions consistent, such as the same users or the same input data.

  Option C, however, compares the performance of a single ML system on two different input datasets. This changes the test conditions rather than the system variant, and therefore does not represent an A/B testing scenario. Instead, it reflects dataset evaluation or robustness testing.

  Thus, Option C is the least representative of A/B testing.

• Question 54:

  Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

  A. It has been trained to recognize cats and is given an image of a dog
  B. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution
  C. It has been trained to analyze mathematical models and is given a set of landscape pictures to classify
  D. It has been trained to analyze customer buying trend data and is given information on supplier cost data
  B. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution

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  The syllabus explains that input changes that are in the same domain as what was used for training are expected to be handled with adaptability:

  "Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data."

  (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)

• Question 55:

  Which ONE of the following approaches to labeling requires the least time and effort?

  A. Outsourced
  B. Pre-labeled dataset
  C. Internal
  D. AI-assisted
  B. Pre-labeled dataset

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  The ISTQB CT-AI syllabus describes multiple approaches to data labeling for supervised learning. Among these approaches, using a pre-labeled dataset requires the least time and effort because the labeling activity has already been completed. This eliminates the need for additional manual labeling, coordination with external resources, or human review of automatically generated labels.

  Other approaches---internal labeling, outsourced labeling, and AI-assisted labeling---still require time and effort for labeling, verification, or quality control. Therefore, they are more resource-intensive than using data that is already labeled.

  Thus, Pre-labeled dataset (Option B) is the correct answer.

• Question 56:

  Which of the following aspects is a challenge when handling test data for an AI-based system?

  A. Personal data or confidential data
  B. Output data or intermediate data
  C. Video frame speed or aspect ratio
  D. Data frameworks or machine learning frameworks
  A. Personal data or confidential data

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  The ISTQB CT-AI syllabus explicitly highlights challenges related to handling personal and confidential data when testing AI-based systems. Testing often requires realistic data sets that reflect real-world usage, but such data frequently contains personal or sensitive information. The syllabus notes that it is difficult to fully anonymize data while preserving its usefulness for testing purposes, and that privacy, legal, and regulatory constraints must be carefully managed during test data handling.

  Therefore, managing personal data or confidential data represents a significant challenge in AI-based testing, which directly corresponds to Option A.

  The other options are not identified by the syllabus as primary challenges related to test data handling. Output or intermediate data management is a general testing concern, video frame speed or aspect ratio relates to media processing rather than data governance, and data or ML frameworks concern tooling rather than test data handling.

• Question 57:

  Which machine learning approach is most suitable for predicting customer purchase probability?

  A. Supervised learning (regression)
  B. Supervised learning (classification)
  C. Unsupervised learning
  D. Reinforcement learning
  B. Supervised learning (classification)

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  The ISTQB CT-AI syllabus explains in Section 1.6 - Machine Learning Approaches that supervised learning is appropriate when labeled data exists and the goal is to predict an output based on known historical examples. Predicting a customer's purchase probability is a classification task when the output corresponds to discrete categories such as "likely to purchase" versus "not likely to purchase." The syllabus gives similar examples in describing classification as the process of assigning instances to

  predefined classes based on learned patterns in labeled data. Because the retail company wants to determine whether a customer will make a purchase based on marketing actions, classification is the most appropriate choice.

  Option A (regression) predicts continuous numeric values and is less suitable because the task centers on categorical likelihood rather than estimating exact monetary values. Option C (unsupervised learning) is used when labels are not available; in this scenario, the company has labeled purchase histories. Option D (reinforcement learning) requires an interactive environment with reward-driven behavior, which is not applicable to this scenario.

  Thus, supervised learning (classification) is the most suitable approach according to the syllabus.

• Question 58:

  Which ONE of the following activities is MOST relevant when you have more than the required amount of data available for training?

  A. Feature selection
  B. Data sampling
  C. Data labeling
  D. Data augmentation
  B. Data sampling

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  The ISTQB CT-AI syllabus explains that data sampling is used to select a representative subset of a larger dataset. When more data is available than required for training, sampling helps reduce the dataset size while preserving its statistical properties, making training more efficient and manageable.

  The other activities are less relevant in this scenario. Feature selection focuses on reducing the number of input features rather than the volume of data. Data labeling is required for supervised learning but does not address excess data. Data augmentation is typically used when there is insufficient data, not when there is an excess.

  Therefore, Data sampling (Option B) is the most relevant activit

• Question 59:

  You are testing an autonomous vehicle that uses AI to determine appropriate driving actions and responses. You have evaluated the parameters and combinations to be tested and determined that there are too many to test within the available time. It has been suggested that you use pairwise testing to reduce the number of parameter combinations. Given the complexity of the system under test, what is the most likely outcome of using pairwise testing?

  A. The number of parameters to test can be reduced to fewer than a dozen.
  B. All high-priority defects will be identified using this method.
  C. While the number of required tests can be reduced, there may still be a sufficiently large test set that automation will be required to execute all of them.
  D. Pairwise testing cannot be applied because AI is involved and evolving values will result in unverifiable outcomes.
  C. While the number of required tests can be reduced, there may still be a sufficiently large test set that automation will be required to execute all of them.

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  The ISTQB CT-AI syllabus explains that pairwise testing is an effective test design technique for reducing the number of test cases when a system has a large number of input parameters. By focusing on all possible pairs of parameter values, many defects caused by two-way interactions can be identified without exhaustive testing.

  However, the syllabus also emphasizes that even after applying pairwise testing, the resulting test suite for complex systems---such as autonomous vehicles with many environmental, sensor, and behavioral parameters---can still be large. In such cases, test automation and virtual test environments are often necessary to execute the reduced but still substantial set of test cases efficiently.

  Option C correctly reflects this guidance.

  Option A is incorrect because pairwise testing reduces combinations, not the number of parameters themselves.

  Option B is incorrect because pairwise testing does not guarantee detection of all high-priority defects, especially those caused by higher-order interactions. Option D is incorrect because pairwise testing is applicable to AI-based systems and is explicitly discussed in the syllabus as a useful technique despite AI complexity.

  Therefore, Option C is the correct answer.

• Question 60:

  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.