Guaranteed Success in ISTQB AI Testing CT-AI Exam Dumps [Q73-Q89] – IT Certification Exam Questions & Answers

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Guaranteed Success in ISTQB AI Testing CT-AI Exam Dumps

ISTQB CT-AI Daily Practice Exam New 2026 Updated 162 Questions

ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester – AI Testing
Exam Number:	CT-AI
Related Certifications:	ISTQB CTFL ISTQB CTAL-TTA
Passing Score:	65%
Available Languages:	English
Exam Format:	Multiple Choice
Certificate Validity Period:	Lifetime (no expiration)
Exam Duration:	60 minutes
Real Exam Qty:	40
Exam Price:	EUR 250
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

Q73. Before deployment of an AI based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?

Explainability

Autonomy

Self-learning

Non-determinism

Explainability in AI-based systems refers to the ease with which users can determine how the system reaches a particular result. It is a crucial aspect when demonstrating AI decision-making, as it ensures that decisions made by AI models are transparent, interpretable, and understandable by stakeholders.
Before deploying an AI-based system, a developer must validate how decisions are made in a test environment. This process falls under the characteristic of explainability because it involves clarifying how an AI model arrives at its conclusions, which helps build trust in the system and meet regulatory and ethical requirements.
* ISTQB CT-AI Syllabus (Section 2.7: Transparency, Interpretability, and Explainability)
* “Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result”.
* “Most users are presented with AI-based systems as ‘black boxes’ and have little awareness of how these systems arrive at their results. This ignorance may even apply to the data scientists who built the systems. Occasionally, users may not even be aware they are interacting with an AI- based system”.
* ISTQB CT-AI Syllabus (Section 8.6: Testing the Transparency, Interpretability, and Explainability of AI-based Systems)
* “Testing the explainability of AI-based systems involves verifying whether users can understand and validate AI-generated decisions. This ensures that AI systems remain accountable and do not make incomprehensible or biased decisions”.
* Contrast with Other Options:
* Autonomy (B): Autonomy relates to an AI system’s ability to operate independently without human oversight. While decision-making is a key function of autonomy, the focus here is on demonstrating the reasoning behind decisions, which falls under explainability rather than autonomy.
* Self-learning (C): Self-learning systems adapt based on previous data and experiences, which is different from making decisions understandable to humans.
* Non-determinism (D): AI-based systems are often probabilistic and non-deterministic, meaning they do not always produce the same output for the same input. This can make testing and validation more challenging, but it does not relate to explaining the decision-making process.
Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question explicitly asks about the characteristic under which decision-making falls when being demonstrated before deployment,explainability is the correct choicebecause it ensures that AI decisions are transparent, understandable, and accountable to stakeholders.

Q74. A team of software testers is attempting to create an AI algorithm to assist in software testing.
This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required thus reducing the time needed for each testing cycle. How can an AI-based tool be expected to assist in this reduction?

By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category

By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases

By performing bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases

By using A/B testing to compare the last update with the newest change and compare metrics between the two

Q75. Which ONE of the below is MOST likely to be a challenge in ML data preparation?

The time and associated costs required

Functional performance

Designing a reward function

Specification of acceptance criteria

Q76. Which ONE of the below types of testing is NOT a type of experience-based testing applied to an AI-based system?

Exploring training data to understand its variety, patterns, structure and shape – then using that to inform testing

Usability testing of the format of the output predictions

Using knowledge about previous ML systems to identify potential biases

Using an industry checklist to assess the steps used to prepare an ML system

Q77. Which of the below TWO examples of AI system behaviour are reward hacking?
i. An AI medical device intended to keep a patient stable may give the patient a treatment that means they recover less quickly.
ii. An AI system intended to maximise production of a commodity by weight allows quality and size of product to reduce.
iii. An AI system intended to ensure the output of a factory process is always sorted correctly, destroys the outputs.
iv. An AI system intended to remove security vulnerabilities from software code, removes all functionality that has security vulnerabilities.

I, I

I, III

III, IV

II, IV

Q78. In a conference on artificial intelligence (Al), a speaker made the statement, “The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?
SELECT ONE OPTION

This statement is incorrect. Current Al is true Al and there is no reason to believe that this fact will change over time.

This statement is correct. In general, what is considered Al today may change over time.

This statement is incorrect. What is considered Al today will continue to be Al even as technology evolves and changes.

This statement is correct. In general, today the term Al is utilized incorrectly.

Q79. 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?

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.

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.

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.

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.

Q80. Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI based system?

Obtaining test data is harder

It is necessary to measure how changes in the test inputs cause changes in the test outputs

Unexpected changes in system behaviour can occur

Sometimes the system can only be tested as a black-box

Q81. A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?

There is no risk, as the model has already been trained

Insufficient function; the model was not trained to check for colors or words

Improper data preparation

Inherited bias: the model could have inherited unknown defects

Q82. Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

Evaluating the model

Deploying the model

Tuning the model

Data testing

Q83. “AllerEgo” is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION

The difficulty of defining criteria for improvement before the model can be accepted.

The fast pace of change did not allow sufficient time for testing.

The unknown nature and insufficient specification of the operating environment might have caused the poor performance.

There was an algorithmic bias in the Al system.

Q84. 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?

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.

Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers.

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.

Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas.

Q85. 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?

Internal data labeling

Crowdsourced data labeling

Outsourced data labeling

AI-assisted data labeling

Q86. 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?

Prepare and pre-process the data that will be used to train and test the model

Tune the machine learning algorithm based on objectives and business priorities

Agree on defined acceptance criteria for the machine learning model

Evaluate the selection of the framework and the model

Q87. Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?
Choose ONE option (1 out of 4)

Generating test cases for the system before and after the change, since neither has a test oracle

Comparing outputs before and after the change using different inputs

Comparing outputs before and after the change using identical inputs

Comparing outputs of a non-self-learning system with those of the changed self-learning system

Q88. 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

Q89. 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.

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	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 3	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 4	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 5	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 6	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 7	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 8	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 9	systems from those required for conventional systems.
Topic 10	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 11	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based