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SASInstitute A00-485 Exam Syllabus Topics:

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. What are some properties that can be defined in a linear regression model?

A) Learning rate and number of iterations
B) Maximum depth and minimum leaf size
C) Coefficients, p-values, and R-squared
D) Confusion matrix and accuracy

2. Prior to fitting a regression model, data binning of a continuous effect should be used for what purpose?

A) Reduce the impact of minor observation errors.
B) Improve the accuracy of your predictions.
C) Reduce the cardinality of a continuous effect.
D) Gain information due to discretization.

3. How can you assign a group-by variable to a predictive model in SAS Visual Analytics?

A) Specify the group-by variable in the data source properties
B) Select the group-by variable from a dropdown menu in the model settings
C) Click on the group-by variable and choose "Assign as predictor" from the context menu
D) Drag and drop the group-by variable into the predictor field of the model

4. What is the purpose of reviewing Measure Details when preparing data for a model?

A) To check data types and formats of the measures
B) To understand the level of measurement for each variable
C) To identify missing values in the measures
D) To determine the aggregation method for measures

5. How can you interpret the impurity measures in an icicle plot of a decision tree?

A) Lower impurity implies more homogeneity within a leaf node.
B) Higher impurity indicates better separation of classes.
C) Impurity measures represent the number of data points in a leaf node.
D) Impurity measures are irrelevant in decision tree analysis.

Solutions: