Question 1

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

Question 2

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 3

A predictive modeler is preparing to set up automated scoring scripts that will run in a production environment. The modeler needs to access a database that contains the data to be scored and has been instructed to use the ODBC interface. Which of the following SAS statements correctly establishes a connection for data access using the LIBNAME statement?

Options :

A :

libname scoring odbc datasrc=mydatasource user=myusername password=mypassword;

B :

libname scoring base 'mydatasource';

C :

proc sql; connect to dbms(datasrc=mydatasource user=myusername password=mypassword); disconnect from dbms; quit;

D :

filename mydblib odbc datasrc=mydatasource user=myusername password=mypassword;

Answer: A

Question 4

You have developed a linear regression model to predict home prices based on various features such as square footage, number of bedrooms, and location. Which of the following summary statistics would best allow you to assess the overall fit of your linear regression model?

Options :

A :

The p-value for each predictor variable in the model.

B :

The R-squared (coefficient of determination) statistic.

C :

The standard error of the regression.

D :

The F-test statistic for the overall model significance.

Answer: B

Question 5

You are applying PCA on a new dataset as part of a dimensionality reduction effort. Given an eigenvalue decomposition of the covariance matrix, what criterion is often used to decide how many principal components to retain?

Options :

A : Select principal components until a cumulative explained variance of at least 70-90% is reached.

B : Always choose the number of principal components that is equal to the original number of variables.

C : Retain the principal components with eigenvalues greater than the average of all eigenvalues.

D : Keep all principal components, as each additional component will increase the predictive power of the model.

Answer: A

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