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

ISTQB ISTQB-CT-AI Online Questions & Answers

• Question 51:

  A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).

  The AI service determines this score from the following input values:

  Reference speed v_max in km/h

  Average speed v_mean in km/h

  Average acceleration a_pos in m/s?

  Average braking deceleration a_neg in m/s?

  The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.

  Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.

  

  Which of the new tests is NOT a follow-up test case for metamorphic testing?

  A. Test A is not a follow-up test case.
  B. Test C is not a follow-up test case.
  C. Test B is not a follow-up test case.
  D. Test D is not a follow-up test case.
  C. Test B is not a follow-up test case.

  ---

  According to the ISTQB CT-AI syllabus, metamorphic testing works by applying metamorphic relations (MRs): predictable input transformations that should lead to predictable output changes. From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:

  v_mean decreases,

  a_pos decreases,

  a_neg becomes less negative,

  and decreases when the opposite occurs.

  A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one metamorphic relation while keeping the expected output direction predictable.

  Test A lowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the metamorphic relation that lower speed leads to a higher score.

  Test C increases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.

  Test D modifies acceleration and braking magnitudes in ways consistent with Test 3's defensive-driving scoring boundaries.

  Test B, however, changes multiple variables in contradictory directions:

  v_mean increases (worse),

  a_pos increases (worse),

  a_neg becomes less negative (better).

  Because these changes conflict, the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.

  Thus, Test B cannot be considered a metamorphic follow-up test, which makes Option C correct.

• Question 52:

  Which of the following are ML functional performance metrics used for classification problems? (Choose three)

  A. Precision
  B. Recall
  C. R-squared
  D. Specificity
  A. Precision
  B. Recall
  D. Specificity

  ---

• Question 53:

  Which of the following options is an example of the concept of overfitting?

  A. A model for predicting academic performance was trained with data from students at one university. The model shows low predictive accuracy when applied to other universities.
  B. A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
  C. A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.
  D. A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.
  A. A model for predicting academic performance was trained with data from students at one university. The model shows low predictive accuracy when applied to other universities.

  ---

  The ISTQB CT- AI syllabus defines overfitting in Section 3.2 - ML Model Evaluation as a condition where a machine learning model learns the training data too precisely---including noise and irrelevant detail---resulting in poor performance on unseen data. Overfitting is characterized by high accuracy on the training data but low accuracy on validation data or real-world data. Option A perfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from

  other universities. This is a textbook example of overfitting because the model has effectively memorized patterns specific to a narrow data set rather than learning generalizable relationships applicable across environments.

  Option B describes sample bias or insufficient diversity in the training data rather than overfitting. Option C involves transfer learning or model extension and is unrelated to overfitting. Option D indicates insufficient training data quality or inadequate features but does not represent overfitting. Only Option A directly reflects the syllabus definition of overfitting: overly specialized training leading to reduced predictive performance on new data.

  Thus, Option A is the correct and syllabus-aligned example of overfitting.

• Question 54:

  You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?

  A. It could identify the objects in multiple ways and then determine the most commonly used and stable identification for each object.
  B. It could modify the automation code to ignore unrecognizable objects to avoid failures.
  C. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
  D. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
  A. It could identify the objects in multiple ways and then determine the most commonly used and stable identification for each object.

  ---

  The ISTQB CT-AI syllabus discusses the use of AI-based tools to reduce the brittleness of GUI-based test automation. Frequent failures often occur when identifiers such as IDs, XPaths, or class names change between releases.

  The syllabus states that:

  "AI can be used to reduce the brittleness of this approach by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."

  This directly aligns with Option A, which describes identifying GUI objects using multiple attributes and selecting the most stable and reliable identifiers over time.

  The other options are incorrect because ignoring objects reduces test value, modifying source code is not a tester responsibility, and predicting developer changes is unrealistic and unsupported by the syllabus.

  Therefore, Option A is the correct and syllabus-aligned answer.

• Question 55:

  An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases. Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

  A. Labeling the data correctly
  B. Minimizing the amount of time spent training the algorithm
  C. Selecting the correct data pipeline for the ML training
  D. Grouping similar products together before feeding them into the algorithm
  A. Labeling the data correctly

  ---

  The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:

  "In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."

  (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

• Question 56:

  Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

  A. Associating shoppers with their shopping tendencies
  B. Grouping individual fish together based on their types of fins
  C. Classifying muffin purchases based on the perceived attractiveness of their packaging
  D. Estimating the expected purchase of cat food after a particularly successful ad campaign
  A. Associating shoppers with their shopping tendencies

  ---

  The ISTQB CT-AI syllabus defines clustering as an unsupervised learning approach in which similarities among input data points are identified in order to group them based on common characteristics or attributes. The syllabus explicitly notes that clustering is often used in marketing scenarios, such as grouping customers according to behavior or tendencies.

  Option A directly matches this definition, as associating shoppers with their shopping tendencies involves identifying natural groupings in customer behavior without predefined labels.

  Option B is incorrect because grouping fish by fin types implies predefined categories, which aligns more with classification than clustering. Option C is also a classification problem, as it involves assigning purchases to predefined categories based on perceived attractiveness. Option D describes a prediction task involving numerical estimation, which is a regression problem rather than clustering.

  Therefore, Option A is the correct example of a clustering problem solvable using unsupervised learning.

• Question 57:

  A beer company is trying to understand how much recognition its logo has in the market. It plans to do this by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a large word across the middle with a bold blue and magenta border. Which associated risk is most likely to occur when using this pre-trained model?

  A. There is no risk, as the model has already been trained.
  B. Insufficient function: the model was not trained to check for colors or words.
  C. Improper data preparation.
  D. Inherited bias: the model could have inherited unknown defects.
  D. Inherited bias: the model could have inherited unknown defects.

  ---

  According to the ISTQB CT-AI syllabus, pre-trained models may inherit biases, limitations, or defects from the data sets, assumptions, and processes used during their original training. Section 1.8.3 - Pre-trained Models explains that users of a pre-trained model typically do not have full visibility into or control over the original training data or training process. As a result, such models can carry forward unknown biases or inaccuracies that may not be appropriate for a new context or use case.

  In this scenario, although the pre-trained model appears technically suitable for detecting logos based on words and colors, the most significant risk is that it may have inherited unknown defects or biases from its original training, which could affect recognition accuracy or reliability when applied to this specific brand and market context.

  Therefore, Option D correctly identifies the most likely associated risk.

• Question 58:

  Which of the following characteristics of AI-based systems makes it more difficult to ensure they are safe?

  A. Simplicity
  B. Sustainability
  C. Non-determinism
  D. Robustness
  C. Non-determinism

  ---

  The ISTQB CT-AI syllabus states that non-determinism is one of the key characteristics that makes ensuring safety in AI-based systems more difficult. Non-deterministic behavior means that the same input may lead to different outputs, which complicates predictability, verification, and validation.

  The syllabus explicitly lists the following characteristics as challenges to safety assurance in AI-based systems: complexity, non-determinism, probabilistic behavior, self-learning, lack of transparency, interpretability and explainability, and lack of robustness.

  Simplicity and sustainability do not inherently introduce safety risks. Robustness is a desired quality attribute; rather, a lack of robustness increases safety risk.

  Therefore, Non-determinism (Option C) is the correct answer.

• Question 59:

  A tourist calls an airline to book a ticket and is connected to an automated system that is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations due to a tight labor market, the only response from the automated system is, "I don't understand your question." This AI system should be categorized as:

  A. General AI
  B. Narrow AI
  C. Super AI
  D. Conventional AI
  D. Conventional AI

  ---

  The ISTQB CT-AI syllabus defines Narrow AI (also known as weak AI) as AI systems designed to perform specific tasks within a clearly defined domain, without the ability to generalize beyond that scope.

  In this scenario, the system can:

  recognize speech,

  understand ticket-purchasing requests,

  provide travel options.

  However, it fails to handle questions outside its designed domain, such as weather forecasting or labor market impacts. This behavior is a defining characteristic of Narrow AI.

  General AI would be able to understand and reason across domains.

  Super AI would exceed human-level intelligence.

  Conventional AI refers to rule-based or deterministic systems and is not the appropriate classification here, as the system clearly uses AI techniques such as speech recognition and natural language understanding.

  Therefore, the correct classification is Narrow AI (Option B).

• Question 60:

  Arihant Meditation is a start-up using AI to aid people in achieving deeper and more effective meditation based on the analysis of various factors, such as the time and duration of meditation, pulse, blood pressure, EEG patterns, and others. Their model accuracy and other functional performance parameters have not yet reached the desired level.

  Which ONE of the following factors is NOT a factor affecting the ML functional performance?

  A. The data pipeline
  B. The quality of the labeling
  C. Biased data
  D. The number of classes
  D. The number of classes

  ---

  The ISTQB CT-AI syllabus explains that several factors directly influence machine learning functional performance, including the data pipeline, the quality of data labeling, and the presence of biased data. Problems in the data pipeline can introduce noise or inconsistencies, poor labeling quality can mislead the learning process, and biased data can distort model outcomes and reduce accuracy or fairness.

  The number of classes, however, is primarily a characteristic of the problem formulation and model design. While it may influence model complexity or architecture, it is not considered a direct factor affecting functional performance metrics such as accuracy, precision, recall, or bias in the sense described by the syllabus.

  Therefore, Option D is the correct answer.