The Realest Study Materials CT-AI Dumps Updated Jul 23, 2026
LATEST CT-AI Exam Practice Material
NEW QUESTION # 34
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?
- A. Prepare and pre-process the data that will be used to train and test the model
- B. Agree on defined acceptance criteria for the machine learning model
- C. Tune the machine learning algorithm based on objectives and business priorities
- D. Evaluate the selection of the framework and the model
Answer: A
Explanation:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non-ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model."
NEW QUESTION # 35
Which of the following describes the AI effect?
Choose ONE option (1 out of 4)
- A. The ability of AI to defeat a human, e.g., in chess
- B. The changing perception of what constitutes AI
- C. The ability of AI to learn from data itself
- D. The fact that mankind can build intelligent machines
Answer: B
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.
NEW QUESTION # 36
Which of the following aspects is a challenge when handling test data for an AI-based system?
- A. Output data or intermediate data
- B. Video frame speed or aspect ratio
- C. Personal data or confidential data
- D. Data frameworks or machine learning frameworks
Answer: C
Explanation:
The syllabus explicitly mentions challenges of handling personal data and ensuring privacy when testing AI-based systems:
"The management of personal data and sensitive data is often a concern during testing, as testing typically requires realistic data and it is difficult to fully anonymize data."
NEW QUESTION # 37
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare between the two versions of the algorithm to see whether or not the update should remain in place. This is an example of what type of testing?
- A. A/B testing
- B. Metamorphic testing
- C. Pairwise testing
- D. Exploratory testing
Answer: A
Explanation:
The syllabus states:
"A/B testing can be used to test updates to an AI-based system where there are agreed acceptance criteria, such as ML functional performance metrics, as described in Chapter 5. A/B testing is used to compare the updated variant with the previous variant."
NEW QUESTION # 38
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?
- A. All high priority defects will be identified using this method.
- B. 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.
- C. 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.
- D. The number of parameters to test can be reduced to less than a dozen.
Answer: B
Explanation:
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:
NEW QUESTION # 39
Which ONE of the following options is a technology used to implement AI?
- A. Genetic algorithms
- B. Autonomy
- C. Reinforcement learning
- D. Classification
Answer: A
Explanation:
Genetic algorithms are a technology used in AI, particularly in optimization problems and machine learning models. They are inspired by the process of natural selection and evolve solutions over generations.
NEW QUESTION # 40
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION
- A. A fully automated manufacturing plant that uses no software.
- B. A system that utilizes a tool like Selenium.
- C. A system that is fully able to respond to its environment.
- D. A system that utilizes human beings for all important decisions.
Answer: C
Explanation:
* AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.
NEW QUESTION # 41
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Choose ONE option (1 out of 4)
- A. Deductive classifiers
- B. Linear regression
- C. Random Forest
- D. Genetic algorithms
Answer: A
Explanation:
TheISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options-Linear regression,Random Forest, andGenetic algorithms-are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus,Deductive classifiers (Option A)is the only technology classified as a reasoning technique.
NEW QUESTION # 42
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
- A. Only III
- B. Only II
- C. I and II
- D. I and III
Answer: C
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
References:
* ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
NEW QUESTION # 43
Which statement about testing levels for AI-based systems is correct?
Choose ONE option (1 out of 4)
- A. Acceptance testing checks non-functional requirements such as explainability
- B. If AI is offered as a service, system testing includes API tests of the service
- C. ML model testing ensures that the relevant ML functional performance criteria are met
- D. Input data testing checks whether the inputs from the data pipeline are received by the model correctly and exchanged with all system components
Answer: C
Explanation:
Section4.3 - Test Levels for AI Systemsclearly defines ML model testing as the level at which testers evaluate whether an ML model fulfills itsfunctional performance criteria, including accuracy, precision, recall, F1, robustness, stability, and fairness. Therefore, Option C is the correct and syllabus-aligned statement.
Option A is incorrect because input data testing focuses onvalidity and correctness of data entering the model, not interactions with all system components. Option B is incorrect: acceptance testing in the syllabus focuses primarily onbusiness and stakeholder requirements, not specifically explainability. Explainability testing may occur at multiple levels depending on context. Option D is also incorrect because API testing belongs tointegration testing, not system testing, even when AI is consumed as a service.
Thus,Option Cis the only statement that precisely matches syllabus definitions.
NEW QUESTION # 44
Which ONE of the following describes a situation of back-to-back testing the LEAST?
- A. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
- B. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
- C. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
- D. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
Answer: C
Explanation:
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions.
Let's analyze the options given:
A). Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B). Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C). Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D). Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to- back testing.
NEW QUESTION # 45
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare between the two versions of the algorithm to see whether or not the update should remain in place.
This is an example of what type of testing?
- A. A/B testing
- B. Metamorphic testing
- C. Pairwise testing
- D. Exploratory testing
Answer: A
Explanation:
A/B testing is a statistical testing method that compares two different versions of a system to determine which one performs better. In this scenario, theold NLP algorithmwas rated for accuracy, and after the update, the new algorithmwas also rated by users. A statistical test was performed to compare the two versions, which is the fundamental approach ofA/B testing.
A/B testing is commonly used in:
* User experience testing(e.g., comparing different versions of a website).
* ML model evaluation(e.g., comparing two AI-based classifiers).
* Performance assessment(e.g., determining if a new recommendation algorithm is more effective).
This approach allows for data-driven decisions, ensuring that any changes to the system result in meaningful improvements.
* Section 9.4 - A/B Testingstates that A/B testing is used to compare updates in AI-based systems to determine if the newer version is better.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 46
Consider an AI-system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?
- A. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure
- B. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-system
- C. The tester wishes to better understand the logic of the software used to create the internal structure
- D. Test automation can be built quickly and easily from the test cases developed during black-box testing
Answer: B
Explanation:
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)
NEW QUESTION # 47
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)
- 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 particularly affects testing of autonomous systems
- C. Automation bias affects the testing of AI-based systems that support users in their actions or decisions
- D. Automation bias is tested with representative users, but human input quality is irrelevant
Answer: C
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.
NEW QUESTION # 48
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labelling approaches can be used?
- A. Augmentation
- B. Benchmarking
- C. Internal
- D. Annotation
Answer: D
Explanation:
The correct answer is B. Annotation . The CT-AI syllabus explains that data labelling enriches unlabeled or poorly labelled data by adding labels so that it becomes suitable for supervised learning. For image-based supervised learning, the syllabus specifically describes labelling objects in images by drawing rectangles around them as a common labelling technique, known as annotation . It also notes that more complex annotation may be required for 3D objects or irregular bounding boxes, and that data labelling and annotation are typically supported by tools.
This directly matches the football-game scenario: the ML engineer must identify the location of the football in each image, which requires object localization rather than merely assigning the whole image to a class.
Option A, "internal," describes who performs labelling within an organization, not the image-labelling technique itself. Option C, augmentation, creates additional data points from an existing dataset, such as transformed images, but it does not identify the football's location. Option D, benchmarking, is unrelated to labelling and is used for comparative evaluation.
References/topics: CT-AI Syllabus Chapter 4, Section 4.5 "Data Labelling for Supervised Learning"; Appendix B "Annotation."
NEW QUESTION # 49
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?
- A. Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes
- B. Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions
- C. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers
- D. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas
Answer: C
Explanation:
The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:
"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)
NEW QUESTION # 50
Which ONE of the following options describes a scenario of A/B testing the LEAST?
- A. A comparison of the performance of an ML system on two different input datasets.
- B. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
- C. A comparison of two different websites for the same company to observe from a user acceptance perspective.
- D. A comparison of the performance of two different ML implementations on the same input data.
Answer: A
Explanation:
Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
NEW QUESTION # 51
......
Study HIGH Quality CT-AI Free Study Guides and Exams Tutorials: https://passleader.testkingpdf.com/CT-AI-testking-pdf-torrent.html
