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ISTQB CT-AI Dumps Questions Answers

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Certified Tester AI Testing Exam

Last Update Jul 23, 2024
Total Questions : 40 With Comprehensive Analysis

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Total Questions : 40 With Comprehensive Analysis

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Certified Tester AI Testing Exam Questions and Answers

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

Options:

A.

Only III

B.

I and II

C.

I and III

D.

Only II

Questions 2

"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

Options:

A.

Maximize precision and accuracy

B.

Maximize accuracy and recall

C.

Maximize recall and precision

D.

Maximize specificity number of classes

Questions 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

Options:

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 to the training data.

D.

These examples are model specific and are not likely to cause another model trained on same task to fail.