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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:

1. Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

A) Using a pixel comparison of the GUI before and after the change to check the differences.
B) Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
C) Using a computer vision to compare the GUI before and after the test object changes.
D) Using a vision-based detection of the GUI layout changes before and after test object changes.

2. Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

A) Self-learning systems are expected to deal with new situations without explicitly having to program for it.
B) Al systems require changing of operational environments; therefore, flexibility is required.
C) Al systems are inherently flexible.
D) Flexible Al systems allow for easier modification of the system as a whole.

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

A) Evaluating the model
B) Data testing
C) Tuning the model
D) Deploying the model

4. A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION

A) Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
B) Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
C) Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
D) Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Solutions: