Question 1

When integrating SAS and R to perform advanced predictive modeling, it's crucial to correctly set up the SAS environment to communicate with R. Assuming that the R software is properly installed on the same machine as SAS, which of the following steps is essential to allow SAS to properly connect to and execute R scripts?

Options :

A :

Set the RHOME system option to the path where R is installed.

B :

Execute the 'library(SASxport)' command in R before calling it from SAS.

C :

Ensure that SAS is installed with the R integration option selected during setup.

D :

Update the PATH system variable to include the directory where the Rscript executable is located.

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

A data scientist is working on a predictive modeling project with a target variable that follows a multinomial distribution because the target variable represents multiple unordered categories. Which procedure should they use to model this type of distribution while maintaining computational efficiency?

Options :

A :

PROC GLM

B :

PROC LOGISTIC

C :

PROC HPSPLIT

D :

PROC HPLOGISTIC

Answer: D

Question 4

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?

Options :

A :

The scoring process will automatically become faster due to fewer cases to process.

B :

Predictions made by the model may be less reliable due to the difference in data quality.

C :

The model will automatically discard any new records with missing values in the scoring dataset.

D :

There will be no issues as predictive models are robust to changes in the rate of missing data.

Answer: B

Question 5

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

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