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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

1. Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION

A) Black box attacks based on adversarial examples create an exact duplicate model of the original.
B) These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
C) These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
D) These examples are model specific and are not likely to cause another model trained on same task to fail.

2. An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION

A) This is an example of expert system bias.
B) This is an example of sample bias.
C) This is an example of hyperparameter bias.
D) This is an example of algorithmic bias.

3. Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

A) Compliance needs require proper care to be taken of input personal data.
B) Data for the use case is being generated at a fast pace.
C) Test data being sourced from public sources.
D) Nature of data constantly changes with lime.

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) Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
C) Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
D) 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.

Solutions: