Question 1

Which of the following is the definition of accuracy?

Options :

A : (True Positives + False Positives) / Total Predictions

B : (True Positives + True Negatives) / Total Predictions

C : True Positives / (True Positives + False Negatives)

D : True Positives / (True Positives + False Positives)

Answer: B

Question 2

Which of the following is NOT a valid cross-validation method?

Options :

A : Bootstrapping

B : K-fold

C : Leave-one-out

D : Stratification

Answer: D

Question 3

Which of the following best describes distributed artificial intelligence?

Options :

A : It does not require hyperparemeter tuning because the distributed nature accounts for the bias.

B : It intelligently pre-distributes the weight of starting a neural network.

C : It relies on a distributed system that performs robust computations across a network of unreliable nodes.

D : It uses a centralized system to speak to decentralized nodes.

Answer: C

Question 4

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 5

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

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