100% Free SAS Institute A00-225 Practice Test Questions

Question 1

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?

A : Use a normal distribution with an identity link function and adjust the model for overdispersion.

B : Use a Poisson distribution with a log link function, and apply a scale parameter to adjust for overdispersion.

C : Use a negative binomial distribution with a log link function.

D : Use a Poisson distribution with a square root link function and adjust the model for overdispersion.

Answer: C

Question 2

You are building a predictive model using a dataset that contains numerous variables. Upon inspection, you notice a significant number of them are highly correlated. Which of the following actions is the MOST appropriate to address potential issues with irrelevant or redundant variables before proceeding with model building?

A : Remove all variables with a correlation coefficient above 0.8 to eliminate redundancy.

B : Use a dimensionality reduction technique like Principal Component Analysis (PCA).

C : Scale all variables to have zero mean and unit variance without considering their correlations.

D : Increase the model complexity to account for the additional information in the correlated variables.

Answer: B

Question 3

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?

A : Training mode, because the model needs to be retrained on the new dataset before it can be scored.

B : Training mode with cross-validation, to ensure that the model generalizes well to the new dataset.

C : Output mode, as the model is already trained and the goal now is to use the model to predict new outcomes.

D : Output mode, but with added regularization parameters to avoid overfitting on the new dataset.

Answer: C

Question 4

You are building a predictive model using a dataset that contains numerous variables. Upon inspection, you notice a significant number of them are highly correlated. Which of the following actions is the MOST appropriate to address potential issues with irrelevant or redundant variables before proceeding with model building?

A : Remove all variables with a correlation coefficient above 0.8 to eliminate redundancy.

B : Use a dimensionality reduction technique like Principal Component Analysis (PCA).

C : Scale all variables to have zero mean and unit variance without considering their correlations.

D : Increase the model complexity to account for the additional information in the correlated variables.

Answer: B

Question 5

You are evaluating the relationship between a binary target variable and a continuous predictor variable using an empirical logit plot. The plot shows that for low values of the predictor, the logit of the target variable is also low, and as the predictor value increases, the logit of the target variable increases linearly. However, for high values of the predictor, the logit of the target variable levels off, forming an S-shape. Which of the following interpretations is correct?

A : The predictor variable has no relationship with the target variable.

B : The relationship between the predictor and the target is linear throughout.

C : There appears to be a threshold after which the predictor's impact on the logit diminishes.

D : The predictor variable is perfectly predicting the target variable.

Answer: C

Free Practice SAS Institute A00-225 Exam Questions 2025