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

ISQI ISQI-CT-AI Online Questions & Answers

• Question 21:

  Which ONE of the following models BEST describes a way to model defect prediction by analyzing the history of bugs in modules, using code quality metrics of historical versions as input?

  A. Identifying the relationship between developers and the modules developed by them.
  B. Searching for similar code using natural language processing.
  C. Clustering similar code modules to predict defects based on similarity.
  D. Using a classification model to predict the presence of a defect by using code quality metrics as input data.
  D. Using a classification model to predict the presence of a defect by using code quality metrics as input data.

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  The ISTQB CT-AI syllabus explains that defect prediction commonly uses supervised learning classification models trained on historical defect data. In this approach, previous versions of software modules are labeled as defective or non- defective, and code quality metrics (such as complexity, size, or coupling) are used as input features.

  A classification model learns patterns from this historical data and predicts whether a given module is likely to contain defects. This directly matches Option D.

  The other options do not accurately describe this approach. Analyzing developer-module relationships does not focus on code metrics. Natural language processing of code similarity is not the standard technique for defect prediction.

  Clustering is an unsupervised technique and does not use labeled defect history in the way described.

  Therefore, Option D is the correct answer.

• Question 22:

  Which data-labeling approach uses a two-step process where labeling is first done by a tool and then verified or completed by a human?

  A. Internal data labeling
  B. Crowdsourced data labeling
  C. Outsourced data labeling
  D. AI-assisted data labeling
  D. AI-assisted data labeling

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  Section 2.4 - Data Labeling Approaches of the ISTQB CT-AI syllabus explicitly defines AI-assisted data labeling as a hybrid approach in which an automated tool performs the initial labeling, and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while maintaining human oversight to ensure data quality. The syllabus presents this approach as an effective compromise when fully manual labeling would be too slow or costly and when automation

  can pre-label obvious patterns before human validation.

  Option A (internal data labeling) refers to labeling performed by the organization's own staff and does not inherently involve automation. Option B (crowdsourced data labeling) relies on a distributed group of human labelers, typically without an automated pre-labeling step. Option C (outsourced data labeling) delegates labeling tasks to an external provider but likewise does not imply an AI-first process. Only Option D reflects the automated-then-human workflow explicitly described in the syllabus.

  Therefore, AI-assisted data labeling (Option D) is the correct answer.

• Question 23:

  Which statement describes factors related to test data that make testing AI-based systems difficult?

  A. Using the same implementation for data acquisition by data scientists and testers prevents defect masking.
  B. Creating and managing large amounts of test data can be difficult, especially when it needs to be representative.
  C. The input data must always be the same over time, especially in real-world systems.
  D. Artificially generated data requires legal approval and must be sanitized and encrypted.
  B. Creating and managing large amounts of test data can be difficult, especially when it needs to be representative.

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  Section 2.2 - Data Preparation and Section 4.1 - Challenges in Testing AI-Based Systems describe difficulties in obtaining and managing large, representative data sets. AI-based systems require realistic, diverse, and representative data that reflects real-world variation. The syllabus emphasizes that assembling such data sets is time-consuming, resource-intensive, and often constrained by availability, privacy concerns, or domain complexity. Option B directly corresponds to these documented challenges.

  Option A is incorrect because using the same implementation for data acquisition can lead to defect masking rather than preventing it; the syllabus explicitly warns against this risk. Option C is incorrect because real-world input data naturally changes over time, and the syllabus notes that data drift is normal in operational environments; expecting stable input data contradicts this reality. Option D is incorrect because, while data privacy is important, the syllabus does not state that artificially generated data

  always requires legal approval or that sanitization and encryption are mandatory in all cases.

  Thus, Option B accurately reflects the syllabus-defined difficulties related to test data in AI-based systems.

• Question 24:

  A software component uses machine learning to recognize digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?

  A. Reinforcement learning
  B. Regression
  C. Classification
  D. Clustering
  C. Classification

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  Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:

  Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).

  Why Not Other Options:

  Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.

  Regression: This is used for predicting continuous values, not discrete categories like digit recognition.

  Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.

  References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

• Question 25:

  A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem. Which ONE of the following options represents the BEST possible solution for this above-mentioned task?

  A. Regression
  B. Association
  C. Clustering
  D. Classification
  C. Clustering

  ---

  Regression is used to predict continuous numerical values and is not suitable for grouping people based on similar tastes.

  Association techniques identify relationships or co-occurrence patterns between variables (for example, market basket analysis), but they do not directly group individuals into segments.

  Clustering is an unsupervised learning technique that groups data points based on similarity in their features without requiring predefined labels. This makes it ideal for identifying groups of people with similar tastes for targeted marketing purposes.

  Classification is a supervised learning approach that assigns data points to predefined classes and requires labeled training data. In this scenario, the goal is to discover groups rather than assign known labels.

  Therefore, Clustering (Option C) is the most appropriate and syllabus-aligned solution.

• Question 26:

  A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed

  vehicle traveling to selected destinations.

  The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner against the actual routes and times.

  Which of the following describes the next phase of metamorphic testing?

  A. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.
  B. The team decomposes each route into the relevant components that affect the travel time, such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.
  C. The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.
  D. The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.
  A. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.

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  The ISTQB CT-AI syllabus describes metamorphic testing as a technique where metamorphic relations (MRs) are defined and then applied to verify that predictable input transformations lead to predictable output changes, even when exact expected outputs are not available.

  In this scenario, vehicle speed is the defining metamorphic relation. By applying the same route to vehicles with different speeds and predicting the expected change in travel time based on speed differences, the testers can verify whether the AI system behaves consistently with the defined relation.

  Therefore, applying speed-based transformations to the same route and validating the resulting arrival times correctly represents the next phase of metamorphic testing, making Option A the correct answer.

• Question 27:

  Which statement regarding data preparation in the ML workflow is correct?

  A. A key challenge in data transformation is the removal or correction of erroneous data.
  B. Since data preparation is time-consuming, all steps should be automated.
  C. One challenge of data gathering is obtaining high-quality data from multiple sources.
  D. Sampling is so well researched that it is no longer considered risky.
  C. One challenge of data gathering is obtaining high-quality data from multiple sources.

  ---

  The ISTQB CT-AI syllabus describes the ML data preparation workflow in Section 2.2 - Data Preparation. Data preparation consists of data gathering, cleaning, transformation, and sampling. The syllabus emphasizes that one significant challenge during data gathering is combining data from multiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring that the resulting data set is accurate, complete, and representative can be complex, making this a critical challenge in the machine learning workflow. This aligns directly with Option C.

  Option A is incorrect because the removal or correction of erroneous data is part of data cleaning, not data transformation. Option B contradicts the syllabus because, although automation can support data preparation, not all steps should be fully automated due to the need for expert judgment and oversight, particularly in identifying subtle data quality issues. Option D is incorrect because sampling remains risky, especially with respect to representativeness and bias, and the syllabus explicitly highlights the need for caution when sampling data.

  Thus, Option C is the only statement that accurately reflects the syllabus.

• Question 28:

  In the near future, technology will have evolved, and AI will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of AI are similar to those of a child aged 1-2 years. In the above quote, which ONE of the following options is the correct name for this type of AI?

  A. Technological singularity
  B. Narrow AI
  C. Super AI
  D. General AI
  D. General AI

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  Technological singularity refers to a hypothetical future point at which AI surpasses human intelligence and is able to improve itself autonomously at an accelerating rate. This concept goes far beyond the capabilities described in the quote.

  Narrow AI (also called weak AI) is designed to perform a specific task or a limited set of tasks. It cannot transfer learning across domains or operate in new environments without retraining, which contradicts the description.

  Super AI refers to an AI that exceeds human intelligence across all cognitive domains. This level of intelligence is significantly more advanced than the child-like cognitive abilities described.

  General AI (also known as strong AI) is defined as an AI system capable of understanding, learning, and applying knowledge across a wide range of tasks and domains, and of adapting to new environments without retraining. This directly aligns with the description in the quote, including the comparison to early human cognitive development.

  Therefore, General AI is the correct answer.

• Question 29:

  The training of an ML model... What type of bias is LEAST important to look for when testing the model?

  A. Inappropriate bias
  B. Automation bias
  C. Algorithmic bias
  D. Sample bias
  B. Automation bias

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  The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, including sample bias, algorithmic bias, and inappropriate bias. In Section 3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate from training data, model development, and decision logic. Sample bias occurs when the training data does not adequately represent the target population. Algorithmic bias arises when the model produces systematically skewed results due

  to learned patterns. Inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect the outputs of the AI model and are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. Such biases can result in missed defects or false alarms, significantly impacting quality assurance.

  Automation bias, however, is fundamentally different. It refers to a human cognitive bias in which users (for example, inspectors) overly trust or rely on the AI system's output. While automation bias is important in user-interaction and human- AI collaboration testing, it is not a bias within the ML model itself. Since the question asks which bias is least important to look for when testing the model, automation bias can be deprioritized in model-level testing.

  Therefore, Option B is correct.

• Question 30:

  Which supervised-learning classification/regression statement is correct?

  A. Recognizing a dog from many different images is a regression problem.
  B. Deciding whether an object is a bicycle or a motorcycle is a classification problem.
  C. Predicting that diesel prices will increase by approximately 10% is a classification problem.
  D. In classification, objects are always assigned to exactly two classes.
  B. Deciding whether an object is a bicycle or a motorcycle is a classification problem.

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  The ISTQB CT-AI syllabus explains supervised learning in Section 1.6 - Machine Learning Approaches. It defines classification as predicting categorical labels, whereas regression predicts continuous numerical values. Option B---deciding whether an object is a bicycle or a motorcycle---fits the definition of classification precisely because the model selects between discrete categories. The syllabus provides similar examples to illustrate classification tasks, confirming this interpretation.

  Option A is incorrect because recognizing a dog in images is a classification task, not a regression task. Option C is incorrect because predicting a price increase of approximately 10% involves forecasting a numerical value, which is a regression problem. Option D is incorrect because classification is not limited to two classes; the syllabus explicitly describes multiclass classification.

  Therefore, Option B is the only statement aligned with the syllabus definitions.