Exam CT-AI Simulations | CT-AI Test Dump

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ISTQB CT-AI Exam Syllabus Topics:

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

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CT-AI Test Dump - Latest CT-AI Questions

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

A. The size of the application is consuming too much of the phone's storage capacity.
B. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.
C. The feedback requires a physical connection and cannot be sent over the Internet.
D. Mobile operating systems cannot process machine learning algorithms.

Answer: B

Explanation:
The syllabus highlights that on-device training and processing require considerable computational power, which may exceed the capabilities of some mobile devices:
"Self-learning and continuous learning systems require large amounts of computational power, which can impact system performance and stability if the hardware is not powerful enough."

NEW QUESTION # 125
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above- mentioned task?

A. Regression
B. Association
C. Classification
D. Clustering

Answer: D

Explanation:
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.

NEW QUESTION # 126
Which challenge to testing self-learning systems puts you at risk of a data attack?
Choose ONE option (1 out of 4)

A. Complex test environment
B. Unexpected changes
C. Inadequate specification of the operating environment
D. Insufficient testing time

Answer: B

Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift.
The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. OptionD - Unexpected changescorresponds directly to this syllabus-defined risk.
Option A refers to system specification issues but does not relate to data attacks. Option B discusses environment complexity, which makes testing difficult but is not tied to adversarial threats. Option C (insufficient testing time) affects quality but does not specifically increase vulnerability to malicious data manipulation.
Unexpected changes-including data drift, poisoned samples, or maliciously constructed training data-pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, causing model degradation or manipulation. Therefore,Option Dcorrectly identifies the challenge that increases exposure to data attacks.

NEW QUESTION # 127
Which statement describes factors related to test data that make testing AI-based systems difficult?

A. Using the same implementation for data acquisition by data scientists and testers prevents defect masking
B. Creating and managing large amounts of test data can be difficult, especially when it needs to be representative
C. The input data must always be the same over time, especially in real-world systems
D. Artificially generated data requires legal approval and must be sanitized and encrypted

Answer: B

Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity. Option B directly corresponds to these documented challenges.

NEW QUESTION # 128
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?

A. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
B. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
C. It could modify the automation code to ignore unrecognizable objects to avoid failures.
D. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.

Answer: B

Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."

NEW QUESTION # 129
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