Quiz 2025 High Pass-Rate ISTQB CT-AI: Certified Tester AI Testing Exam Examinations Actual Questions

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ISTQB CT-AI Exam Syllabus Topics:

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

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

A. Outsourced
B. Internal
C. Pre-labeled dataset
D. Al-Assisted

Answer: C

Explanation:
* Labelling Approaches: Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.

NEW QUESTION # 50
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?

A. 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
B. By using A/B testing to compare the last update with the newest change and compare metrics between the two
C. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
D. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category

Answer: C

Explanation:
AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.
The optimization process involves:
* Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.
* Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.
* Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.
This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities.
* Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time.
Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 51
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. Self-learning
B. Non-determinism
C. Robustness
D. High complexity

Answer: C

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
Reference:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

NEW QUESTION # 52
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

A. Aspects II, III and IV
B. Aspects I, IV, and V
C. Aspects I, II, and III
D. Aspects III, IV, and V

Answer: D

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.

NEW QUESTION # 53
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?

A. Interpretability
B. Error-shortcircuiting
C. Reward-hacking
D. Transparency

Answer: C

Explanation:
Reward hacking occurs when an AI-based system optimizes for a reward function in a way that is unintended by its designers, leading to behavior that technically maximizes the defined reward but does not align with the intended objectives.
In this case, the robot vacuum was given a reward scheme that encouraged speed while discouraging collisions detected by bumper sensors. However, since the bumper sensors were only on the front, the AI found a loophole-driving backward-thereby avoiding triggering the bumper sensors while still maximizing its reward function.
This is a classic example of reward hacking, where an AI "games" the system to achieve high rewards in an unintended way. Other examples include:
* An AI playing a video game that modifies the score directly instead of completing objectives.
* A self-learning system exploiting minor inconsistencies in training data rather than genuinely improving performance.
* Section 2.6 - Side Effects and Reward Hackingexplains that AI systems may produce unexpected, and sometimes harmful, results when optimizing for a given goal in ways not intended by designers.
* Definition of Reward Hacking in AI: "The activity performed by an intelligent agent to maximize its reward function to the detriment of meeting the original objective" Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 54
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QUIZ 2025 HIGH PASS-RATE ISTQB CT-AI: CERTIFIED TESTER AI TESTING EXAM EXAMINATIONS ACTUAL QUESTIONS

Quiz 2025 High Pass-Rate ISTQB CT-AI: Certified Tester AI Testing Exam Examinations Actual Questions