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Question 1

The choice of ML framework, algorithm, and hyperparameters is critical as a requisite to a better system. The choice of the parameters should be as close to optimal as possible.Which test level for AI-based systems ensure the BEST ML framework and hyperparameters are integrated with other features and components?

Options :

A : Component integration testing

B : ML Model Testing

C : Component testing

D : System testing

Answer: B

Question 2

“The activity of building an ML model using a process of trial and reward to achieve an objective ” :

Options :

A : Random Forest

B : Supervised Learning

C : Unsupervised Learning

D : Reinforcement Learning

Answer: D

Question 3

What is the correct definition for the term "False negative"?

Options :

A : An ML model prediction in which the model mistakenly predicts the negative class

B : An ML functional performance metric used to evaluate a classifier which provides a balance between recall and precision

C : The interactive, hypothesis-driven and visual exploration of data used to support feature engineering

D : The field of study related to understanding the factors that influence AI system outputs

Answer: A

Question 4

Which of the following options CORRECTLY states how an AI-based tool can perform optimization of regression test suites?

Options :

A : By analyzing information from previous testing activities

B : By using genetic algorithms to create new test cases

C : By analyzing false positive test results

D : By updating the expected results to counter concept drift

Answer: A

Question 5

Which of the following statements BEST describes how system complexity can create challenges when testing an AI-based system?

Options :

A : It can be difficult to find representative data to train a model

B : Testing for bias may require data that the team does not have

C : Determining whether a system is ethical can be subjective

D : Manual generation of white-box tests can be difficult

Answer: D