Question 1

How does Amazon Personalize improve customer engagement?

Options :

A : By identifying potentially fraudulent activities in real-time.

B : By providing highly accurate demand forecasts.

C : By making it easy to build, train, and deploy recommendation models.

D : By converting text into lifelike speech.

Answer: C

Question 2

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City.One of the random variables is discrete and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.Which prior probability distribution should the ML Specialist use for this variable?

Options :

A : Normal distribution

B : Poisson distribution

C : Uniform distribution

D : Binomial distribution

Answer: D

Question 3

Which AWS service makes it easy to build and deploy recommendation models?

Options :

A : Amazon Personalize

B : Amazon Forecast

C : Amazon Fraud Detector

D : Amazon SageMaker

Answer: A

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 characterizes a simple machine learning model?

Options :

A : Low accuracy but high explainability

B : High accuracy and high explainability

C : Low accuracy and low explainability

D : High accuracy but low explainability

Answer: A

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