Question 1

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?

Options :

A : &EM;_DATA_INPUT

B : &EM;_IMPORT_DATA

C : &EM;_DATA_TABLE

D : &EM;_MACRO_VAR

Answer: A

Question 2

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?

Options :

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

Question 3

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

Options :

A :

Forests of trees have fewer parameters to tune and are easier to deploy.

B :

Forests of trees generally perform better on small datasets.

C :

Forests of trees can be easily parallelized, reducing computation time.

D :

Forests of trees provide a deterministic outcome, unlike gradient boosting.

Answer: C

Question 4

You have built several predictive models using training data and now you want to assess the models' performance using validation data. Which measure is NOT appropriate for comparing model performance in terms of model bias for continuous outcomes?

Options :

A :

Root Mean Squared Error (RMSE)

B :

Akaike’s Information Criterion (AIC)

C :

Mean Absolute Error (MAE)

D :

Coefficient of Determination (R-squared)

Answer: B

Question 5

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?

Options :

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

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