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

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 3

You need to implement a logistic regression model in SAS to predict customer churn. During model development, you have to ensure the model accounts for the effects of multicollinearity. Which PROC LOGISTIC option should you use to diagnose multicollinearity and provide a measure to identify problematic variables?

Options :

A :

The COLLIN statement to produce collinearity diagnostics

B :

The SELECTION=SCORE option to perform variable selection based on score statistics

C :

The VIF option in the MODEL statement to obtain variance inflation factors

D :

The OUTEST= option to export the estimation results to a dataset

Answer: C

Question 4

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 5

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?

Options :

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

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