Free SAS Institute A00-225 Exam Questions

When using SAS Visual Statistics to build a generalized linear model for count data, which model settings should you choose to properly account for overdispersion in the data if the initial model assessment indicates that the variance is greater than the mean?

You are performing predictive modeling using a random forest technique in R through SAS Enterprise Miner. You want to examine variable importance to interpret the model. Which of the following commands within the R code can provide you with the variable importance measure typically associated with a random forest model?

When preparing data for a predictive modeling project, a data scientist notices that the categorical variable 'payment_type' with four categories ('credit card', 'debit card', 'paypal', 'other') exhibits a high degree of variability in the outcome variable (purchase amount). To improve the model's predictive accuracy, what strategy can the data scientist use to handle the 'payment_type' variable?

When preparing to score a new dataset with a predictive model you have previously built, you notice that one of the predictor variables has a higher rate of missing values than in the training set used to build the model. What potential issue should you be wary of?

You are required to use PROC NEURAL in SAS to construct a neural network model that minimizes overfitting. Which combination of options would be most effective for this purpose?