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ISTQB AI Testing ISTQBCertified Tester AI Testing Exam

ISTQBCertified Tester AI Testing Exam

Last Update Sep 14, 2025
Total Questions : 80

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Questions 2

Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?

SELECT ONE OPTION

Options:

A.  

Value coverage

B.  

Threshold coverage

C.  

Sign change coverage

D.  

Neuron coverage

Discussion 0
Questions 3

Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?

SELECT ONE OPTION

Options:

A.  

Feature selection

B.  

Data sampling

C.  

Data labeling

D.  

Data augmentation

Discussion 0
Questions 4

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?

Options:

A.  

By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category

B.  

By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases

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

Discussion 0
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Questions 5

Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?

SELECT ONE OPTION

Options:

A.  

Evaluating the model

B.  

Deploying the model

C.  

Tuning the model

D.  

Data testing

Discussion 0

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