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

1. A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

A) Only II
B) Only III
C) I and III
D) I and II

2. Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

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

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

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

Solutions: