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

ISQI ISQI-CT-AI Online Questions & Answers

• Question 61:

  A startup company has implemented a new facial recognition system for a banking application on mobile devices. The application is intended to learn at run time on the device to determine whether the user should be granted access. It also sends feedback over the Internet to the application developers. After deployment, the application caused continuous restarts of the mobile devices. Which of the following is the most likely cause of the failure?

  A. The feedback requires a physical connection and cannot be sent over the Internet.
  B. Mobile operating systems cannot process machine learning algorithms.
  C. The size of the application is consuming too much of the phone's storage capacity.
  D. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.
  D. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.

  ---

  The ISTQB CT-AI syllabus explains that on-device training and continuous learning can require substantial computational resources. When training, inference, and diagnostic generation are all performed locally on constrained hardware such as mobile devices, the workload may exceed CPU, GPU, memory, or thermal limits. This can lead to severe performance degradation, instability, or repeated system restarts.

  The other options are incorrect. Internet feedback does not require a physical connection, modern mobile operating systems can execute ML workloads, and storage size alone would not typically cause continuous restarts.

  Therefore, the most likely cause is that the computational demands exceed the capabilities of the mobile device hardware, making Option D correct.

• Question 62:

  Which statement about automation bias is correct?

  A. When testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis.
  B. Automation bias affects the testing of AI-based systems that support users in their actions or decisions.
  C. Automation bias particularly affects testing of autonomous systems.
  D. Automation bias is tested with representative users, but human input quality is irrelevant.
  B. Automation bias affects the testing of AI-based systems that support users in their actions or decisions.

  ---

  Automation bias is defined in Section 4.4 --- Human Factors in AI Testing of the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to outputs provided by automated systems. The syllabus explains that this bias arises especially in decision-support systems, where humans may accept AI-generated recommendations or judgments without sufficient verification. This aligns directly with Option B. Option A is incorrect because automation bias does influence testing activities, particularly when testers or users rely excessively on AI outputs. The syllabus explicitly warns that testers themselves may adopt the same cognitive biases as end users. Option C is incorrect because automation bias is most relevant in systems that support human decision-making rather than fully autonomous systems. Option D is incorrect because the quality of human input is highly relevant; poorly designed user studies or low-quality interaction data can obscure or distort the presence of automation bias.

  Thus, Option B is the syllabus-accurate statement.

• Question 63:

  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

  ---

  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 64:

  "BioSearch" is creating an AI model used for predicting cancer occurrence by examining X-ray images. The accuracy of the model in isolation has been found to be good. However, users of the model started complaining about the poor quality of results---especially the inability to detect real cancer cases---when it

  was put into practice in the diagnosis lab, leading to the discontinuation of the model's usage.

  A testing expert was called in to identify deficiencies in the test planning that led to this situation.

  Which ONE of the following options would you expect to be MOST LIKELY identified by the test expert?

  A. A lack of similarity between the training and testing data.
  B. The input data was not tested for quality prior to being used for testing.
  C. A lack of focus on choosing the right functional performance metrics.
  D. A lack of focus on non-functional requirements testing.
  A. A lack of similarity between the training and testing data.

  ---

  The scenario describes a model that shows good accuracy in isolation but performs poorly in real-world operation, particularly in detecting true cancer cases. This strongly indicates a generalization problem.

  A lack of similarity between the training and testing data means that the training data was not sufficiently representative of real-world diagnostic data. This is a well-known ML risk that results in models performing well during development but failing in operational environments.

  While input data quality (Option B) is important, the issue described is not primarily about corrupted or invalid data but about representativeness.

  Choosing inappropriate functional performance metrics (Option C) could hide problems, but it would not by itself cause poor real-world detection if the data were representative.

  Non-functional requirements testing (Option D) is important but does not directly explain the inability to detect real cancer cases.

  The ISTQB CT-AI syllabus (Section 4.2 - Training, Validation, and Test Datasets) emphasizes that a lack of representative data across training, validation, and operational environments is a major cause of poor real-world ML performance.

  Therefore, Option A is the most likely deficiency discovered by the test expert.

• Question 65:

  Which statement regarding flexibility and adaptability of AI-based systems is correct?

  A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.
  B. Adaptability is considered to be the ability of the system to be used in unspecified situations.
  C. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.
  D. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
  A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.

  ---

  The ISTQB CT-AI syllabus defines flexibility and adaptability in Section 2.1 - Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situations not explicitly covered by its original requirements, whereas adaptability refers to how easily the system can be modified to handle new environments or operating conditions. The syllabus emphasizes that both properties are especially important for self-learning AI-based systems, which may need to adjust their behavior autonomously

  as conditions change.

  It further explains that such systems must be capable of determining when and how to adapt their behavior in evolving or previously unknown situations. This directly aligns with Option A, which highlights the importance of both adaptability and flexibility when a system needs to change its behavior and determine those changes on its own.

  Option B is incorrect because it reverses the definitions: operating in unspecified situations relates to flexibility, not adaptability. Option C is incorrect because self-learning AI-based systems are not classified as either adaptable-only or flexible-only; they typically require both characteristics. Option D is incorrect because ease of reprogramming corresponds to adaptability, not flexibility.

  Thus, Option A correctly reflects the syllabus definitions.

• Question 66:

  Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?

  A. Testing the distribution shift in the training data for inappropriate bias.
  B. Test the model during model evaluation for data bias.
  C. Testing the data pipeline for any sources for algorithmic bias.
  D. Check the input test data for potential sample bias.
  B. Test the model during model evaluation for data bias.

  ---

  Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.

  Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.

  Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.

  Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.

  Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline. Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias .

• Question 67:

  Which ONE of the following describes a situation of back-to-back testing the LEAST?

  A. Comparison of the results of a current neural network ML model implemented on platform A (for example, PyTorch) with a similar neural network ML model implemented on platform B (for example, TensorFlow), using the same data.
  B. Comparison of the results of a home-grown neural network ML model with the results of a neural network ML model implemented using a standard framework (for example, PyTorch), using the same data.
  C. Comparison of the results of a neural network ML model with a decision tree ML model using the same data.
  D. Comparison of the results of the current neural network ML model using the current dataset with a slightly modified dataset.
  C. Comparison of the results of a neural network ML model with a decision tree ML model using the same data.

  ---

  Back-to-back testing is a technique in which the same test cases and input data are executed against multiple implementations that are intended to provide equivalent behavior, and their outputs are compared to identify inconsistencies.

  Option A describes comparing two implementations of the same type of model (neural networks) built on different platforms using identical data, which is a typical back-to-back testing scenario. Option B describes comparing a custom implementation with a standard reference implementation using the same data, which is also a common use of back-to-back testing to validate correctness.

  Option C describes comparing a neural network model with a decision tree model using the same data.

  Because these models are fundamentally different in structure and behavior, they are not expected to produce equivalent outputs, making this the least appropriate scenario for back-to-back testing.

  Option D compares the same model using different datasets. This represents robustness or sensitivity testing rather than back-to-back testing, but it still does not involve comparing different model types.

  Therefore, Option C is the correct answer, as it least describes a back-to-back testing situation.

• Question 68:

  A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. During test planning, the team realizes that the specifications are insufficient. Which testing approach should be used to test this system?

  A. Exploratory testing
  B. Static analysis
  C. Equivalence partitioning
  D. State transition testing
  A. Exploratory testing

  ---

  The ISTQB CT-AI syllabus explains that exploratory testing is particularly suitable when specifications are incomplete or unclear, or when there are test oracle problems. This situation frequently occurs with AI-based systems, such as chat assistants, where behavior may be probabilistic, data-driven, or insufficiently specified in advance.

  The syllabus explicitly states that exploratory testing is especially useful when there are poor specifications or difficulties in defining expected results. This aligns directly with the described scenario, where the team recognizes that the specifications are insufficient. Therefore, exploratory testing is the most appropriate testing approach, making Option A the correct answer.

• Question 69:

  When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

  A. Test cases to verify that the system automatically confirms the correct classification of training data
  B. Test cases to detect the system appropriately automating its data input
  C. Test cases to detect the system prompting for unnecessary human intervention
  D. Test cases to verify that the system automatically suppresses invalid output data
  C. Test cases to detect the system prompting for unnecessary human intervention

  ---

  The syllabus highlights that testing for unnecessary human intervention is a key focus for autonomous AI- based systems:

  "For autonomous AI-based systems, testers must ensure that the system does not prompt for unnecessary human intervention, as this contradicts the autonomy concept."

  (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.2, page 59 of 99)

• Question 70:

  Which statement regarding pairwise testing in an AI-based automotive lane-keeping assist system is correct?

  A. Pairwise testing reduces the test suite so much that it is typically feasible within the available time.
  B. Pairwise testing can reduce testing efforts otherwise very high due to the large number of parameters.
  C. Pairwise testing only uses parameters directly influenced by the driver; otherwise, the number of test cases becomes too large.
  D. Pairwise testing is usually insufficient because most defects arise only from interactions of many parameters.
  B. Pairwise testing can reduce testing efforts otherwise very high due to the large number of parameters.

  ---

  The ISTQB CT-AI syllabus (Section 4.3 - Test Design for AI-Based Systems) highlights pairwise testing as an effective test-case reduction technique for systems with many input parameters. Lane-keeping assist systems typically include environmental, sensor, and vehicle-dynamic parameters, making exhaustive testing infeasible. Pairwise testing significantly reduces the number of test cases while still capturing all two-way interactions, which are responsible for a large proportion of software defects.

  Option B aligns with this syllabus description: pairwise testing reduces otherwise extremely large parameter combinations, making the testing effort manageable. Option A overstates feasibility guarantees; the syllabus does not claim that pairwise testing always makes testing "typically feasible." Option C is unsupported and incorrect because pairwise testing does not restrict parameters to those controlled by the driver. Option D is incorrect because, although some defects arise from higher-order interactions,

  pairwise testing captures many relevant defects and is widely recognized as a pragmatic compromise.

  Thus, Option B is the correct statement.