Online Access Free DY0-001 Practice Test

A data scientist needs to determine whether product sales are impacted by other contributing factors. The client has provided the data scientist with sales and other variables in the data set.
The data scientist decides to test potential models that include other information.
INSTRUCTIONS
Part 1
Use the information provided in the table to select the appropriate regression model.
Part 2
Review the summary output and variable table to determine which variable is statistically significant.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

Correct Answer:

See explanation below.
Explanation:
Part 1
Linear regression.
Of the four models, linear regression has the highest R² (0.8), indicating it explains the greatest proportion of variance in sales.

Part 2
Var 4 - Net operations cost.
Net operations cost has a p-value of essentially 0 (far below 0.05), indicating it is the only additional predictor statistically significant in explaining sales. Neither inventory cost (p#0.90) nor initial investment (p#0.23) reach significance.

A data scientist built several models that perform about the same but vary in the number of features. Which of the following models should the data scientist recommend for production according to Occam's razor?

• A.The model with the fewest features and the lowest performance
• B.The model with the fewest features and highest performance
• C.The model with the most features and the highest performance
• D.The model with the most features and the lowest performance

A data scientist is working with a data set that covers a two-year period for a large number of machines. The data set contains:
* Machine system ID numbers
* Sensor measurement values
* Daily timestamps for each machine
The data scientist needs to plot the total measurements from all the machines over the entire time period.
Which of the following is the best way to present this data?

• A.Line plot
• B.Box-and-whisker plot
• C.Scatter plot
• D.Histogram

Which of the following does k represent in the k-means model?

• A.Distance between features
• B.Number of clusters
• C.Number of data splits
• D.Number of model tests

The term "greedy algorithms" refers to machine-learning algorithms that:

• A.examine every node of a tree before making a decision.
• B.apply a theoretical model to the distribution of the data.
• C.make the locally optimal decision.
• D.update priors as more data is seen.