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

ISQI ISQI-CT-AI Online Questions & Answers

• Question 131:

  Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?

  A. Determinism
  B. Complexity
  C. Interpretability
  D. Robustness
  B. Complexity

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  The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section 2.8 - Safety and AI explicitly names the characteristics that complicate safety assurance: complexity, non-determinism, probabilistic behavior, self-learning, lack of transparency, and lack of robustness. Among these, complexity is a core challenge because modern AI systems---particularly those using deep learning---exhibit highly non-linear behavior, large numbers of parameters, and intricate interactions that are difficult to predict and analyze.

  Option B (Complexity) directly aligns with the syllabus and is therefore correct. Option A (Determinism) is incorrect because determinism is not a typical characteristic of AI-based systems and does not increase safety risk; in fact, non- determinism is the challenge identified in the syllabus. Option C (Interpretability) affects trust and explainability, but the syllabus treats it as a transparency issue rather than the primary factor that makes safety assurance difficult. Option D (Robustness) is a desired quality attribute; it is the lack of robustness, not robustness itself, that contributes to safety concerns.

  Thus, Complexity best reflects the syllabus-defined difficulty in ensuring the safety of AI-based systems.

• Question 132:

  Which of the following are the three activities in data acquisition for data preparation?

  A. Cleaning, transforming, augmenting
  B. Feature selecting, feature growing, feature augmenting
  C. Identifying, gathering, labeling
  D. Building, approving, deploying
  C. Identifying, gathering, labeling

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  The ISTQB CT-AI syllabus defines data acquisition as the activity of obtaining data relevant to the business problem addressed by an ML model. This activity typically consists of three steps: identifying relevant data sources, gathering the data, and labeling the data for use in supervised learning.

  The other options describe activities that belong to different phases of the ML workflow. Cleaning and transforming are part of data preprocessing, feature-related activities are part of feature engineering, and building or deploying relates to later stages of system development.

  Therefore, Identifying, gathering, labeling (Option C) is the correct answer.

• Question 133:

  Which of the following decisions is BEST as a test approach for the described situation?

  A. You plan to manually execute regular regression tests of the new camera function, particularly for system tests.
  B. You execute the test cases from the old camera model at the integration test level; no further dynamic tests of the operating data pipeline are necessary.
  C. You plan experience-based testing by the entire team at the system test level to ensure that the end users are satisfied.
  D. You plan to perform reviews and exploratory data analysis of the image data sets to reduce the risk of a lack of representativeness of this data.
  C. You plan experience-based testing by the entire team at the system test level to ensure that the end users are satisfied.

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  The ISTQB CT-AI syllabus emphasizes that testing AI-based systems requires cross-functional collaboration and experience-based testing when parts of the team lack domain knowledge. In this scenario, the machine learning expert understands ML and data set preparation but lacks knowledge of camera system behavior, the device's operational data pipeline, and end-user workflows. The remainder of the team understands the domain and system testing but not machine learning.

  Section 4.4 - Human Factors and AI Testing and Section 4.3 - System Testing of AI Components highlight that when domain understanding is unevenly distributed, experience-based testing conducted by the full team (testers, developers, and domain experts) is the most effective approach. This ensures that AI outputs align with actual user expectations and system behavior. Option C aligns exactly with this principle.

  Option A is too limited and does not address the need to validate machine learning integration. Option B is incorrect because reusing old test cases overlooks AI-specific risks in the operating data pipeline. Option D is useful but focuses only on data representativeness rather than system-level user validation. Therefore, Option C is the best syllabus-aligned test approach.