Question 1

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 2

You have built a logistic regression model using PROC LOGISTIC to predict the probability of customer churn based on several predictors. You want to evaluate the performance of the model by analyzing the Receiver Operating Characteristic (ROC) curve. To score a validation dataset and produce the ROC curve, which statement correctly implements the OUTROC option in the SCORE statement?

Options :

A :

SCORE DATA=validation OUT=scored_results OUTROC=roc_curve;

B :

PROC LOGISTIC DATA=training; SCORE OUTROC=roc_curve; RUN;

C :

PROC LOGISTIC DATA=training; SCORE DATA=validation OUTROC=roc_curve; RUN;

D :

SCORE DATA=validation OUT=scored_results; MODEL OUTROC=roc_curve;

Answer: C

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

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 5

You are conducting a time series analysis and need to estimate the parameters of an ARIMA (Autoregressive Integrated Moving Average) model to forecast future sales. Given that the data show signs of non-stationarity and seasonality, which parameter estimation method should you use for the best results?

Options :

A : Ordinary Least Squares (OLS)

B : Maximum Likelihood Estimation (MLE)

C : Generalized Least Squares (GLS)

D : Instrumental Variables Estimation (IVE)

Answer: B

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