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

ISTQB ISTQB-CT-AI Online Questions & Answers

• Question 91:

  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 92:

  Which statement regarding the use of training, validation, and test data sets is correct?

  A. If only limited data is available, validation and test data sets can be combined in multiple ways during training.
  B. If limited data is available, it may be better to work without a separate test data set.
  C. Optimally, the data should be distributed equally between the training, validation, and test data sets.
  D. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.
  D. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.

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  The ISTQB CT-AI syllabus (Section 3.2 - Model Evaluation) specifies the correct usage of training, validation, and test data sets. It emphasizes that the test data set must be representative of the real operational data and must be equivalent in distribution to the training and validation data sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.

  Option A contradicts the syllabus because validation and test data sets serve different purposes: validation is used for model tuning, while the test data set is used for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test data set or using techniques such as cross-validation rather than eliminating testing. Option C is incorrect because an equal distribution between training, validation, and test

  data sets is not recommended; typically, the training data set is significantly larger (for example, 70-80%).

  Thus, Option D is the only statement aligned with the syllabus guidance.

• Question 93:

  Which challenge to testing self-learning systems puts you at risk of a data attack?

  A. Inadequate specification of the operating environment
  B. Complex test environment
  C. Insufficient testing time
  D. Unexpected changes
  D. Unexpected changes

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  The ISTQB CT-AI syllabus describes that self-learning systems continuously adjust their behavior during operation as new data arrives. Section 4.1 - Challenges of Testing AI-Based Systems highlights that such systems are vulnerable to data attacks, particularly through adversarial inputs, data poisoning, or malicious drift. The risk arises because unexpected changes in the input data distribution may alter the learned model in harmful ways. Option D - Unexpected changes corresponds directly to this

  syllabus-defined risk.

  Option A refers to system specification issues but does not relate to data attacks. Option B discusses environmental complexity, which makes testing more difficult but is not directly tied to adversarial threats. Option C (insufficient testing time) affects overall quality but does not specifically increase vulnerability to malicious data manipulation.

  Unexpected changes---including data drift, poisoned samples, or maliciously constructed training data---pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, leading to model degradation or manipulation. Therefore, Option D correctly identifies the challenge that increases exposure to data attacks.

• Question 94:

  An airline has created an ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer, but as winter set in, the load weights became less accurate. After some exploratory data analysis, it became apparent that luggage weights were higher in the winter than in summer.

  Which of the following statements BEST describes the problem and how it could have been prevented?

  A. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated
  B. The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed
  C. The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes
  D. The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated
  A. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated

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  The ISTQB CT-AI syllabus defines concept drift as a situation where the statistical properties of the operational environment change over time while the trained model remains unchanged. As a result, model outputs become less accurate and less useful.

  In this scenario, seasonal changes caused a shift in baggage weight patterns, which were not adequately reflected in the model's assumptions. This is a classic example of concept drift caused by changing real-world conditions.

  The syllabus states that operational ML models should be regularly evaluated against their acceptance criteria so that drift can be detected early and mitigated through retraining, model adjustment, or data updates.

  Therefore, Option A correctly identifies both the problem (concept drift) and the appropriate preventive measure.

• Question 95:

  Which ONE of the following combinations of training, validation, and testing data is used during the process of learning/creating the model?

  A. Training data - validation data - test data
  B. Training data - validation data
  C. Training data - test data
  D. Validation data - test data
  A. Training data - validation data - test data

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  The process of developing a machine learning model typically involves the use of three types of datasets:

  Training data

  Used to train the model by learning patterns and relationships in the data.

  Validation data

  Used to tune hyperparameters and to reduce the risk of overfitting during training.

  Test data

  Used to evaluate the final model's performance and estimate how it will perform on previously unseen data.

  Evaluation of the options:

  A. Training data - validation data - test data

  This option correctly includes all three datasets used during model development: training for learning, validation for tuning, and testing for final evaluation.

  B. Training data - validation data

  This option omits the test data, which is required to assess final model performance on unseen data.

  C. Training data - test data

  This option omits the validation data, which is important for tuning and preventing overfitting.

  D. Validation data - test data

  This option omits the training data, which is essential for learning the model.

  Therefore, the correct answer is Option A, as it includes all datasets used during the process of learning and creating the model.

  Reference ISTQB CT-AI Syllabus v1.0, Sections 3.2 and 4.1

• Question 96:

  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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  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 97:

  Which ONE of the following BEST describes the AI Effect?

  A. The ability of AI systems to outperform humans in complex tasks
  B. The changing perception of what constitutes AI over time
  C. The use of probabilistic algorithms instead of deterministic ones
  D. The requirement that AI systems must be self-learning
  B. The changing perception of what constitutes AI over time

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• Question 98:

  Which of the following characteristics make ensuring safety more difficult in AI-based systems? (Choose two)

  A. Deterministic execution
  B. Self-learning behavior
  C. Probabilistic outputs
  D. Simplicity of design
  B. Self-learning behavior
  C. Probabilistic outputs

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• Question 99:

  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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  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 100:

  You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

  A. Autonomy
  B. Explorability
  C. Transparency
  D. Accessibility
  C. Transparency

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  "Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined." Access to the training data is an example of transparency.