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DY0-001 Practice Exam Questions and Answers

CompTIA DataX Exam

Last Update 3 days ago
Total Questions : 85

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Question # 1

A data scientist is developing a model to predict the outcome of a vote for a national mascot. The choice is between tigers and lions. The full data set represents feedback from individuals representing 17 professions and 12 different locations. The following rank aggregation represents 80% of the data set:

(Screenshot shows survey rankings for just two professions and a few locations, all voting for "Tigers")

Which of the following is the most likely concern about the model's ability to predict the outcome of the vote?

Options:

A.  

Interpolated data

B.  

Extrapolated data

C.  

In-sample data

D.  

Out-of-sample data

Discussion 0
Question # 2

Which of the following is the naive assumption in Bayes' rule?

Options:

A.  

Normal distribution

B.  

Independence

C.  

Uniform distribution

D.  

Homoskedasticity

Discussion 0
Question # 3

Which of the following explains back propagation?

Options:

A.  

The passage of convolutions backward through a neural network to update weights and biases

B.  

The passage of accuracy backward through a neural network to update weights and biases

C.  

The passage of nodes backward through a neural network to update weights and biases

D.  

The passage of errors backward through a neural network to update weights and biases

Discussion 0
Question # 4

A data scientist wants to digitize historical hard copies of documents. Which of the following is the best method for this task?

Options:

A.  

Word2vec

B.  

Optical character recognition

C.  

Latent semantic analysis

D.  

Semantic segmentation

Discussion 0
Question # 5

A data scientist is creating a responsive model that will update a product's daily pricing based on the previous day's sales volume. Which of the following resource constraints is the data scientist's greatest concern?

Options:

A.  

Deployment time

B.  

Training time

C.  

Development time

D.  

Data collection time

Discussion 0
Question # 6

Which of the following best describes the minimization of the residual term in a ridge linear regression?

Options:

A.  

|e|

B.  

e

C.  

D.  

0

Discussion 0
Question # 7

A data analyst is examining the correlation matrix of a new data set to identify issues that could adversely impact model performance. Which of the following is the analyst most likely checking for?

Options:

A.  

Undersampling

B.  

Multicollinearity

C.  

Oversampling

D.  

Overfitting

Discussion 0
Question # 8

A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the data. Which of the following should the data scientist maximize when evaluating the regression performance metrics?

Options:

A.  

Accuracy

B.  

C.  

p value

D.  

AUC

Discussion 0
Question # 9

A data scientist is designing a real-time machine-learning model that classifies a user based on initial behavior. The run times of these models are provided in the following table:

Which of the following models should the data scientist recommend for deployment?

Options:

A.  

XGBoost

B.  

Random forest

C.  

Decision trees

D.  

Artificial neural network

Discussion 0
Question # 10

A data scientist wants to predict a person's travel destination. The options are:

    Branson, Missouri, United States

    Mount Kilimanjaro, Tanzania

    Disneyland Paris, Paris, France

    Sydney Opera House, Sydney, Australia

Which of the following models would best fit this use case?

Options:

A.  

Linear discriminant analysis

B.  

k-means modeling

C.  

Latent semantic analysis

D.  

Principal component analysis

Discussion 0
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