Question 1

When is unexplainability not acceptable?

Options :

A : When determining the result of a sports match

B : When making product recommendations

C : When explaining why a loan was declined

D : When explaining why a transaction was deemed fraudulent

Answer: C,D

Question 2

What is a key advantage of a complex machine learning model?

Options :

A : Speed of data processing

B : High accuracy

C : Low computational requirements

D : High explainability

Answer: B

Question 3

The Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training.The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.What does the Specialist need to do?

Options :

A : Organize the Docker container-s file structure to execute on GPU instances.

B : Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.

C : Build the Docker container to be NVIDIA-Docker compatible.

D : Bundle the NVIDIA drivers with the Docker image.

Answer: D

Question 4

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?

Options :

A : Linear regression

B : K-means

C : Logistic regression

D : Principal component analysis (PCA)

Answer: A

Question 5

What is the significance of predictions in Amazon's ML flywheel?

Options :

A : Predictions drive efficiency improvements and new capabilities.

B : Predictions have minimal impact on business practices.

C : Predictions are discarded after initial use.

D : Predictions are used to validate past performance.

Answer: A

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