Question 1

Word Embedding describes a task in natural language processing (NLP) where:

Options :

A : Words are converted into numerical vectors.

B : Words are featurized by taking a histogram of letter counts.

C : Words are featurized by taking a matrix of bigram counts.

D : Words are grouped together into clusters and then represented by word cluster membership.

Answer: A

Question 2

Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression. What should you do before log-transforming Y?

Options :

A : Add 1 to all of the Y values.

B : Divide all the Y values by the standard deviation of Y.

C : Explore the data for outliers.

D : Subtract the mean of Y from all the Y values.

Answer: A

Question 3

Which of the following equations best represent an LI norm?

Options :

A : |x| + |y|

B : |x|+|y|^2

C : |x|-|y|

D : |x|^2+|y|^2

Answer: A

Question 4

Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?

Options :

A : As new resources become available

B : Once a month

C : When the input format changes

D : When the input volume changes

Answer: C

Question 5

Which two techniques are used to build personas in the ML development lifecycle? (Select two.)

Options :

A : Population estimates

B : Population regression

C : Population resampling

D : Population triage

E : Population variance

Answer: A,D

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