Free ISTQB CT-AI Test Practice Test Questions Exam Dumps [Q41-Q60]

January 9, 2026 CT-AI, ISTQB 0 Comments

Free ISTQB CT-AI Test Practice Test Questions Exam Dumps [Q41-Q60]

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Free ISTQB CT-AI Test Practice Test Questions Exam Dumps

Prepare Top ISTQB CT-AI Exam Audio Study Guide Practice Questions Edition

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 2	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 3	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 4	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 7	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

NO.41 “BioSearch” is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION

A lack of similarity between the training and testing data.

The input data has not been tested for quality prior to use for testing.

A lack of focus on choosing the right functional-performance metrics.

A lack of focus on non-functional requirements testing.

NO.42 You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determinedthat there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

The number of parameters to test can be reduced to less than a dozen.

All high priority defects will be identified using this method.

While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.

Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified.

Pairwise testing is a combinatorial testing technique that reduces the number of test cases by focusing on testing interactions between pairs of parameters rather than all possible combinations. It is widely used in AI- based systems, including autonomous vehicles, where the number of possible input parameter combinations can be extremely high.
* Option A:”The number of parameters to test can be reduced to less than a dozen.”
* This is incorrect. While pairwise testing significantly reduces the number of test cases, it does not necessarily limit them to a fixed number like a dozen. The final number of tests depends on the number of parameters and their possible values.
* Option B:”All high priority defects will be identified using this method.”
* This is incorrect. While pairwise testing is effective in detecting defects caused by interactions between two parameters, it may not uncover defects resulting from more complex interactions involving three or more parameters.
* Option C:”While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.”
* This is the correct answer. Even though pairwise testing reduces the number of test cases, AI- based systems such as autonomous vehicles still have a large number of test scenarios. Therefore, automation is often necessary to execute all test cases within the available time.
* Option D:”Pairwise cannot be applied to this problem because there is AI involved, and the evolving values may result in unexpected results that cannot be verified.”
* This is incorrect. Pairwise testing can still be applied to AI-based systems, including those that evolve over time. However, additional testing techniques may be required to verify evolving behavior.
* Pairwise Testing for AI Systems:”Pairwise testing is widely used because it effectively reduces the number of test cases while maintaining defect detection capability”.
* Automation Requirement:”In practice, even with pairwise testing, extensive test suites may still require automation”.
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

NO.43 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?

It could modify the automation code to ignore unrecognizable objects to avoid failures.

It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.

It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.

It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.

NO.44 A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the “from” portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?

EDA can help detect the outlier emails from the real emails

EDA cannot be used to detect the attack

EDA can restrict how many inputs can be provided by unique users

EDA can detect and remove the false emails

NO.45 Which statement about the property of the test environment for an AI-based system is correct?
Choose ONE option (1 out of 4)

The test environment for an autonomous AI system needs to perform both test design and execution autonomously.

The test environment for an AI system may need to include tools that can explain the decisions of the test object.

The test environment for a self-learning AI system needs to adapt to and learn from the test object.

The test environment for an AI-based multi-agent system needs to act deterministically.

NO.46 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 teamhas 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?

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

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

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

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.

NO.47 An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

Selecting training data purposely excluding specific faulty conditions

Selecting training data by purposely including all known faulty conditions

Selecting testing data from a different dataset than the training dataset

Selecting testing data from a boat manufacturer’s bolt longevity data

NO.48 Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

A system that utilizes human beings for all important decisions.

A fully automated manufacturing plant that uses no software.

A system that utilizes a tool like Selenium.

A system that is fully able to respond to its environment.

NO.49 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?

Autonomy

Explorability

Transparency

Accessibility

NO.50 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?
Choose ONE option (1 out of 4)

Internal data labeling

Crowdsourced data labeling

Outsourced data labeling

AI-assisted data labeling

NO.51 Which of the following statements about reinforcement learning is correct?
Choose ONE option (1 out of 4)

The agent creates a model of the environment from labeled data during training

The approach is suitable when the application doesnotrequire interaction with the environment

The agent’s training is based on a reward function that rewards successful attempts

From experience, the agent learns theoptimal reward function

NO.52 Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION

Training data – validation data – test data

Training data – validation data

Training data * test data

Validation data – test data

NO.53 ln the near future, technology will have evolved, and Al 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 Al are similar to a child of 1-2 years.’ In the above quote, which ONE of the following options is the correct name of this type of Al?
SELECT ONE OPTION

Technological singularity

Narrow Al

Super Al

General Al

NO.54 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?

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

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

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

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

NO.55 Which of the following descriptions of quality aspects of a data set is correct?
Choose ONE option (1 out of 4)

The quality aspect “Incomplete data” describes the fact that data is missing, e.g., for a certain time interval.

The quality aspect “Data not preprocessed” describes the fact that the collected data was recorded incorrectly.

The quality aspect “Irrelevant data” describes the fact that irrelevant data does not affect the ML model.

The quality aspect “Unbalanced data” describes the fact that the data used should be as up-to-date as possible.

NO.56 Which statement regarding pairwise testing in an AI-based automotive lane-keeping assist system is correct?
Choose ONE option (1 out of 4)

Pairwise testing reduces the test suite so much that it is typically feasible within the available time.

Pairwise testing can reduce testing efforts otherwise very high due to the large number of parameters.

Pairwise testing only uses parameters directly influenced by the driver, otherwise the number of test cases becomes too large.

Pairwise testing is usually insufficient because most defects arise only from interactions of many parameters.

NO.57 Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

Compliance needs require proper care to be taken of input personal data.

Nature of data constantly changes with lime.

Data for the use case is being generated at a fast pace.

Test data being sourced from public sources.

NO.58 Which two test procedures are BEST suited for CleverPropose system testing?
Choose TWO options (2 out of 5)

Back-to-back testing

Adversarial testing

Metamorphic testing

Exploratory data analysis

Pairwise testing

NO.59 Which statement regarding flexibility and adaptability of AI-based systems is correct?
Choose ONE option (1 out of 4)

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

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

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

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

NO.60 A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.

What is the precision of this predictive model?