Free SAS Institute A00-225 Exam Questions
Absolute Free A00-225 Exam Practice for Comprehensive Preparation
-
SAS Institute A00-225 Exam Questions
-
Provided By: SAS Institute
- Exam: SAS Advanced Predictive Modeling
- Certification: SAS Administration
-
Total Questions: 347
-
Updated On: May 21, 2026
-
Rated: 4.9 |
-
Online Users: 694
-
Question 1
-
A data analyst is using PROC LOGISTIC to perform logistic regression on a dataset where the response variable is binary. The analyst wants to ensure that the output dataset contains the predicted probabilities for the event of interest along with the total number of events and non-events for each group defined by the CLASS variable. Which combination of statements and options should the analyst use?
Answer: C
-
A data analyst is using PROC LOGISTIC to perform logistic regression on a dataset where the response variable is binary. The analyst wants to ensure that the output dataset contains the predicted probabilities for the event of interest along with the total number of events and non-events for each group defined by the CLASS variable. Which combination of statements and options should the analyst use?
-
Question 2
-
In an R code node within SAS Enterprise Miner, the project variable handle is used to access a data set. Which variable handle correctly imports the data for analysis in the R environment?
Answer: A
-
-
Question 3
-
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?
Answer: B
-
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?
-
Question 4
-
You are analyzing a dataset with a linear regression model to predict sales revenue based on multiple input variables. To prevent overfitting, you decide to include a penalty for including too many variables in the model. Which property adjustment are you most likely to use?
Answer: B
-
You are analyzing a dataset with a linear regression model to predict sales revenue based on multiple input variables. To prevent overfitting, you decide to include a penalty for including too many variables in the model. Which property adjustment are you most likely to use?
-
Question 5
-
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?
Answer: C
-