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ISQI CT-AI_v1.0_World Exam Syllabus Topics:

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

1. "Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION

A) Maximize accuracy and recall
B) Maximize specificity number of classes
C) Maximize recall and precision
D) Maximize precision and accuracy

2. The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION

A) Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
B) Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
C) Individual bias at the neuron level, and weights assigned to the connections between theneurons.
D) Individual bias at the neuron level, and activation values of neurons in the previous layer.

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

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

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

Solutions: