Question 1

Why is explainability important in some contexts?

Options :

A : It improves the model-s accuracy

B : It enhances the model-s speed

C : It reduces the amount of training data needed

D : It ensures that decisions can be justified and understood

Answer: D

Question 2

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor. Which solution will meet this requirement?

Options :

A : Configure the competitor's name as a blocked phrase in Amazon Q Business.

B : Configure an Amazon Q Business retriever to exclude the competitors name.

C :

Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.

D :

Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

Answer: A

Question 3

What role will machine learning play in the future of technology?

Options :

A : It will only be used in healthcare

B : It will be boundless with applications in various fields

C : It will become obsolete

D : It will be limited to fraud detection

Answer: B

Question 4

A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset. Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

Options :

A : Hyperbaric!

B : Grid search

C : Bayesian optimization

D : Random search

Answer: A

Question 5

What characteristic of a problem makes ML an ideal solution?

Options :

A : The problem can be solved with traditional methods.

B : The problem involves complex and dynamic data.

C : The problem does not require scalability.

D : The problem requires simple logic.

Answer: B

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