최신 A00-485 무료덤프 - SASInstitute Modeling Using SAS Visual Statistics

A. Filtering using the toolbar options

B. Exclude option in filter dialogs

C. Interactive filters in the visualization

D. Filter containers

A. Age is a significant predictor of the response.

B. For a 1371.5 unit decrease in Total Promos, there is an expected one-unit increase in the response.

C. The Intercept is an important predictor of the response.

D. For one one-unit increase in Average Sales, there is an expected increase in the response of
4475.443.

A. By providing a visual interface for connecting to external data sources

B. By generating score code automatically during ETL processes

C. By automatically tuning hyperparameters for scoring

D. By enabling the scheduling of batch scoring jobs

A. Category items are not allowed.

B. Interaction items are allowed.

C. A target variable is required.

D. At least two variables are required.

A. The standard deviation of the predictor variables

B. The summary statistics of the response variable

C. The distribution of residuals in the model

D. The performance metrics of the model, such as AUC and KS Statistic

A. Data Source

B. Model Type

C. Response Variable

D. Assessment Bins

E. Link Function

F. Event Level

A. Fit a model for each data segment defined by one or more category variables.

B. Use variable selection.

C. Fit a model for every value of a measure's distribution.

D. Leverage informative missingness for measure and category variables.

A. By importing the code into SAS Data Integration Studio

B. By creating a scoring node in a SAS Enterprise Miner process flow

C. By manually executing the code in a command-line interface

D. By pasting the code into a web browser

A. Before assigning roles to variables

B. Based on the nature of the response variable and the problem

C. After specifying the learning rate

D. After specifying the number of predictors

A. Maximum leaf nodes

B. Maximum depth

C. Maximum tree height

D. Minimum split count

A. Class labels

B. Depth of the tree

C. Feature values

D. Impurity measures

A. Running a SQL query to generate predictions

B. Converting the code to a macro for automation

C. Opening the code file and manually executing it

D. Dragging and dropping the score code file into the project

A. It is calculated using the number of variables visible in the plot.

B. It is calculated using the variable with the greatest number of bins.

C. It is calculated using the number of variables used in the cluster analysis.

D. It cannot exceed 3,000.