Question 1

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?

Options :

A :

Increase the number of iterations.

B :

Apply a LASSO (Least Absolute Shrinkage and Selection Operator) penalty to the regression.

C :

Increase the convergence criterion.

D :

Decrease the size of the training dataset.

Answer: B

Question 2

You are performing predictive modeling using a random forest technique in R through SAS Enterprise Miner. You want to examine variable importance to interpret the model. Which of the following commands within the R code can provide you with the variable importance measure typically associated with a random forest model?

Options :

A :

randomForest::importance(model)

B :

rpart::varImp(model)

C :

caret::varImp(model, scale = FALSE)

D :

varImpPlot(model)

Answer: A

Question 3

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?

Options :

A :

Model A

B :

Model B

C :

Model C

D :

More information is needed regarding the internal structure of the models and the distribution of the data.

Answer: C

Question 4

A data analyst has performed a cluster analysis on a dataset and generated the following cluster matrix:

Cluster 1 Cluster 2

Count 150 200

Mean X 5.0 2.0

Mean Y 2.0 5.0

SSD 20.0 15.0

Given the cluster matrix above, which statement correctly interprets the cluster characteristics?

Options :

A :

Cluster 1 has a larger variability within the cluster than Cluster 2, as indicated by the sum of squares distance (SSD).

B :

Cluster 1 is more densely packed than Cluster 2 because it has a higher count.

C :

The two clusters are likely well separated along the Y dimension given the difference in their mean Y values.

D :

The two clusters are likely well separated along the X dimension given the difference in their mean Y values.

Answer: C

Question 5

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?

Options :

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