Question 1

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 2

What is an indication that ML might be an overcomplicated solution?

Options :

A : The problem involves generating personalized responses.

B : The problem can be solved with traditional algorithms.

C : The problem involves complex and dynamic data.

D : The problem requires quick adaptation to new data.

Answer: B

Question 3

A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements.However, company acronyms are being mispronounced in the current documents.How should a Machine Learning Specialist address this issue for future documents?

Options :

A : Use Amazon Lex to preprocess the text files for pronunciation.

B : Create an appropriate pronunciation lexicon.

C : Output speech marks to guide in pronunciation.

D : Convert current documents to SSML with pronunciation tags.

Answer: D

Question 4

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 5

An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning. The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain. Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

Options :

A : The ML engineer and the Canvas user must be in separate SageMaker domains.

B :

The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.

C : The model must be registered in the SageMaker Model Registry.

D : The ML engineer must host the model on AWS Marketplace.

E : The ML engineer must deploy the model to a SageMaker endpoint.

Answer: B,C

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