SASInstitute Modeling Using SAS Visual Statistics : A00-485

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SASInstitute A00-485 Exam Syllabus Topics:

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. When performing linear regression modeling, what is the primary goal of assessing multicollinearity among predictors?

A) To detect non-linear relationships
B) To identify influential outliers
C) To calculate the coefficient of determination (R-squared)
D) To ensure that predictor variables are not highly correlated

2. What are the benefits of applying filters to both visualization and data source in SAS Visual Analytics?

A) Increased performance by reducing data retrieval from the source
B) Improved security by restricting access to specific data
C) Enhanced user experience by providing more interactive filtering options
D) Consistency in data presentation across multiple visualizations

3. How can score code be implemented to score new data using SAS Enterprise Miner?

A) By importing the code into SAS Data Integration Studio
B) By pasting the code into a web browser
C) By creating a scoring node in a SAS Enterprise Miner process flow
D) By manually executing the code in a command-line interface

4. Which statement is true about performing regressions in SAS Visual Statistics?

A) You can build both a linear regression or a logistic regression with a generalized linear model.
B) You can specify a multinomial classifier as the response variable with linear regression.
C) You can build neither a linear regression nor a logistic regression with a generalized linear model.
D) You can specify an ordinal classifier as the response variable with linear regression.

5. Which SAS tools can be used to score new data using score code generated by SAS Visual Statistics?

A) SAS Enterprise Miner
B) SAS Enterprise Guide
C) SAS Studio
D) SAS Data Integration Studio

Solutions: