Quiz 2026 ISTQB CT-AI: Perfect Testing Certified Tester AI Testing Exam Center

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4	systems from those required for conventional systems.
Topic 5	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 6	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 7	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 8	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9	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 10	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.
Topic 11	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
Which of the following problems would best be solved using the supervised learning category of regression?

A. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.
B. Determining if an animal is a pig or a cow based on image recognition.
C. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.
D. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.

Answer: D

Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."

NEW QUESTION # 98
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

A. Clustering requires you to know the classes.
B. Clustering is supervised learning.
C. Clustering is classification of a continuous quantity.
D. Clustering is done without prior knowledge of output classes.

Answer: D

Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.

NEW QUESTION # 99
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. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
C. Adaptability is considered to be the ability of the system to be used in unspecified situations.
D. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.

Answer: A

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 be modified to 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 determining when and how to adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with Option A.

NEW QUESTION # 100
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 determined that 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. 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.
B. All high priority defects will be identified using this method.
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: A

Explanation:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run."

NEW QUESTION # 101
Which of the following aspects is a challenge when handling test data for an AI-based system?

A. Video frame speed or aspect ratio
B. Data frameworks or machine learning frameworks
C. Output data or intermediate data
D. Personal data or confidential data

Answer: D

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 # 102
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