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

2. 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 III
B) Only II
C) I and III
D) I and II

3. Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

A) Procedural programming
B) Case control structures
C) Search engines
D) Genetic algorithms

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

Solutions: