Free SAS Institute A00-225 Exam Questions

As a data scientist, you have built three predictive models to forecast the risk of a rare event occurring within a patient group. To evaluate these models, you have computed several fit statistics. Consider the following statistics for the models: Model A: - BIC: 182 - AIC: 175 - KS: 0.65 - Brier Score: 0.12 Model B: - BIC: 185 - AIC: 178 - KS: 0.60 Brier Score: 0.11 Model C: - BIC: 180 - AIC: 182 - KS: 0.80 - Brier Score: 0.09 Assuming that the most important criteria for model selection is the prediction accuracy of the rare event and considering the disease is highly imbalanced, which model should you recommend?

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?

In cluster analysis using parallel coordinate plots in SAS, how do you interpret a plot where clusters are shown with overlapping lines across multiple axes?

When comparing different predictive modeling techniques, why might a forest of trees be preferred over a gradient boosting model in certain scenarios?

You are developing a predictive model using SAS and have completed the model training phase. You are now preparing to deploy the model for scoring a new dataset. Which mode should you operate in to score the new dataset and why?