Question 1

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 2

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 3

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 4

A classifier has been implemented to predict whether or not someone has a specific type of disease. Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

Options :

A : Mean squared error

B : Precision and accuracy

C : Precision and recall

D : Recall and explained variance

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