Real ISTQB CT-AI Exam Questions: Ensure Your Success

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q61-Q66):

NEW QUESTION # 61
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

A. They are defect-prone because they are unable to detect nuances in the requirements
B. They are slow and will usually not be able to execute in the time allowed
C. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures
D. They make debugging more complicated because the number of steps is usually high in order to induce the target failure

Answer: C

Explanation:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)

NEW QUESTION # 62
Which of the following is a technique used in machine learning?

A. Decision trees
B. Equivalence partitioning
C. Decision tables
D. Boundary value analysis

Answer: A

Explanation:
Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:
"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)

NEW QUESTION # 63
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 optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
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 Bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
D. By using A/B testing to compare the last update with the newest change and compare metrics between the two

Answer: A

Explanation:
The syllabus mentions that AI can help optimize regression test suites:
"An AI-based tool can perform optimization of the regression test suite by analyzing... the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)

NEW QUESTION # 64
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

A. Testing the speed of the prediction by the model.
B. Testing the speed of the training of the model.
C. Testing the accuracy of the classification model.
D. Testing the API of the service powered by the ML model.

Answer: B

Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
References:
* ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

NEW QUESTION # 65
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered that when they start typing
"Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing.
What type of bias is this?

A. Automation/Complacency
B. Geographical/Locality
C. Complacency/Disregard
D. Ignorance/Cognitive

Answer: A

Explanation:
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision-making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.
(Reference: ISTQB CT-AI Syllabus v1.0, Glossary, Page 89 of 99)

NEW QUESTION # 66
......

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Real ISTQB CT-AI Exam Questions: Ensure Your Success

Updated ISTQB CT-AI CBT, CT-AI Latest Test Format

ISTQB Certified Tester AI Testing Exam Sample Questions (Q61-Q66):

ISTQB CT-AI Exam | Trustworthy CT-AI Dumps - Offer you Valid Updated CT-AI CBT

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