Free Practice Questions for iSQI CT-AI Exam

Question 1

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 with the actual routes and times.

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

AThe 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 then 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.

BThe 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.

CThe 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.

DThe 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.

Correct Answer: 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 then 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.

Explanation:

The syllabus describes metamorphic testing as:

''Testing involves defining metamorphic relations and then applying those relations to check that the transformations result in expected outcomes, even when the expected output of the system is unknown or not well-defined.''

In this scenario, applying the metamorphic relation (speed differences) and checking the transformed outcome (arrival times) fits the definition of metamorphic testing.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.5, page 69 of 99)

Question 2

Which supervised-learning classification/regression statement is correct?

Choose ONE option (1 out of 4)

ARecognizing a dog from many different images is a regression problem

BDeciding whether an object is a bicycle or a motorcycle is a classification problem

CPredicting that diesel prices will increase by ~10% is a classification problem

DIn classification, objects are always assigned to exactly two classes

Correct Answer: B. Deciding whether an object is a bicycle or a motorcycle is a classification problem

Explanation:

The ISTQB CT-AI syllabus explains supervised learning under Section1.6 -- Machine Learning Approaches. It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values. OptionB---deciding whether an object is a bicycle or a motorcycle---fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .

Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregressionproblem. Option D is incorrect because classification can involveany number of classes, not only two. Multiclass classification is explicitly mentioned in the syllabus.

Therefore, OptionBis the only answer aligned with the syllabus' definitions.

Question 3

Which statement about automation bias is correct?

Choose ONE option (1 out of 4)

AWhen testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis

BAutomation bias affects the testing of AI-based systems that support users in their actions or decisions

CAutomation bias particularly affects testing of autonomous systems

DAutomation bias is tested with representative users, but human input quality is irrelevant

Correct Answer: B. Automation bias affects the testing of AI-based systems that support users in their actions or decisions

Explanation:

Automation bias is defined in Section4.4 -- Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.

Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.

Thus,Option Bis the syllabus-accurate description of automation bias.

Question 4

A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two.

What test method should you use to verify that the model has improved after the additional training?

AMetamorphic testing because the application domain is not clearly understood at this point

BAdversarial testing to verify that no incorrect images have been used in the training

CPairwise testing using combinatorics to look at a long list of photo parameters

DBack-to-back testing using the version of the model before training and the new version of the model after being trained with additional images

Correct Answer: D. Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images

Explanation:

The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:

'Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions.'

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.3, page 67 of 99)

Question 5

Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?

SELECT ONE OPTION

ATraining data - validation data - test data

BTraining data - validation data

CTraining data * test data

DValidation data - test data

Correct Answer: A. Training data - validation data - test data

Explanation:

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

Training Data:This is used to train the model, i.e., to learn the patterns and relationships in the data.

Validation Data:This is used to tune the model's hyperparameters and to prevent overfitting during the training process.

Test Data:This is used to evaluate the final model's performance and to estimate how it will perform on unseen data.

Let's analyze each option:

A . Training data - validation data - test data

This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.

B . Training data - validation data

This option misses the test data, which is crucial for evaluating the model's performance on unseen data after the training and validation phases.

C . Training data - test data

This option misses the validation data, which is important for tuning the model and preventing overfitting during training.

D . Validation data - test data

This option misses the training data, which is essential for the initial learning phase of the model.

Therefore, the correct answer isAbecause it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.

Question 6

Which two test procedures are BEST suited for CleverPropose system testing?

Choose TWO options (2 out of 5)

ABack-to-back testing

BAdversarial testing

CMetamorphic testing

DExploratory data analysis

EPairwise testing

Correct Answer: A. Back-to-back testing; C. Metamorphic testing

Explanation:

The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to-back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.

Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 -- Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as ''if customer income increases, risk rating should not worsen.'' These relations allow validation even when exact expected outputs are unavailable.

Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.

Thus,A and Care the correct and syllabus-supported choices.

Question 7

Which of the following describes the AI effect?

Choose ONE option (1 out of 4)

AThe changing perception of what constitutes AI

BThe ability of AI to learn from data itself

CThe fact that mankind can build intelligent machines

DThe ability of AI to defeat a human, e.g., in chess

Correct Answer: A. The changing perception of what constitutes AI

Explanation:

TheAI Effectis clearly defined in theISTQB Certified Tester AI Testing Syllabus v1.0under Section1.1 -- Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as ''AI'' changes over time. Technologies once considered AI---such as expert systems from the 1970s and 1980s or early chess-playing systems---are no longer viewed as AI today. This phenomenon is explicitly labeled the''AI Effect,''described as''the changing perception of what constitutes AI .''The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .

Options B, C, and D do not capture this definition. While AI learning from data (B) is a property of ML, it does not describe the shifting perception of AI . Option C describes a technological achievement, not a perceptual shift. Option D references a historical AI milestone (Deep Blue defeating Kasparov) that the syllabus specifically uses as an example of technology that is no longer considered AI due to the AI Effect. Therefore, onlyOption Aaccurately reflects the AI Effect as defined by the ISTQB syllabus.

Question 8

Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?

SELECT ONE OPTION

ANatural language processing on textual requirements

BAnalyzing source code for generating test cases

CMachine learning on logs of execution

DGUI analysis by computer vision

Correct Answer: A. Natural language processing on textual requirements

Explanation:

When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.

Question 9

Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

AIt has been trained to recognize cats and is given an image of a dog

BIt has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution

CIt has been trained to analyze mathematical models and is given a set of landscape pictures to classify

DIt has been trained to analyze customer buying trend data and is given information on supplier cost data

Correct Answer: B. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution

Explanation:

The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:

'Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data.'

(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)

Question 10

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

Choose ONE option (1 out of 4)

AAdaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.

BAdaptability is considered to be the ability of the system to be used in unspecified situations.

CSelf-learning AI-based systems are classified according to whether they are adaptable only or flexible only.

DFlexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.

Correct Answer: A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.

Explanation:

The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 -- Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can bemodifiedto handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself-learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determiningwhenandhowto adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with OptionA.

Option B reverses definitions---the syllabus states flexibility (not adaptability) relates to unspecified situations. Option C is incorrect: self-learning systems requirebothflexibility and adaptability; they are not categorized as one or the other. Option D incorrectly defines flexibility; the syllabus defines adaptability---not flexibility---as ease of modification.

Thus,Option Acorrectly reflects the syllabus.