CT-AI Practice Exams Free & CT-AI Valid Dumps Questions

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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	systems from those required for conventional systems.
Topic 3	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
You are conducting a user acceptance test of a decision support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?

A. The number of accurate recommendations per day
B. The number of inaccurate recommendations per day
C. The accuracy of the human decision when presented with an inaccurate recommendation
D. The amount of system resources consumed by each recommendation

Answer: C

Explanation:
The correct answer is D . A decision support recommendation system does not merely produce predictions; it influences human decision-making. The CT-AI syllabus explains that automation bias, also called complacency bias, occurs when humans become too trusting of automated recommendations. One form occurs when a human accepts system recommendations and fails to consider other sources, including their own judgment.
For that reason, testing should cover both the quality of the system's recommendations and the quality of the corresponding human input provided by representative users. The syllabus explicitly states that testers should understand how human decision-making may be compromised and should test both the system recommendations and the human input.
Options A and C are still functional performance measures of the recommendation system. Option B is a performance efficiency concern and may be relevant at system testing, but it does not address the decision- support risk. Option D directly targets the critical acceptance concern: whether users can detect and appropriately handle inaccurate AI recommendations rather than blindly following them.
References/topics: CT-AI Syllabus Chapter 7, Section 7.4 "Testing for Automation Bias in AI-Based Systems"; Section 7.2.6 "Acceptance Testing."

NEW QUESTION # 31
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. Transparency
B. Reward-hacking
C. Error-shortcircuiting
D. Interpretability

Answer: B

Explanation:
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a
'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function--driving backwards to avoid bumper triggers while maximizing reward for speed.

NEW QUESTION # 32
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 prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions
B. 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
C. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas
D. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers

Answer: D

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 # 33
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop-off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail. The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

A. The robot must record the time of each delivery which is compiled into a report
B. The robot must complete 99.99% of its deliveries each day
C. The robot must evolve to optimize its routing
D. The robot must recharge for no more than six hours a day

Answer: C

Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3)

NEW QUESTION # 34
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 using A/B testing to compare the last update with the newest change and compare metrics between the two
B. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category
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 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

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.

NEW QUESTION # 35
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