Question 1

A data analyst is using PROC LOGISTIC to perform logistic regression on a dataset where the response variable is binary. The analyst wants to ensure that the output dataset contains the predicted probabilities for the event of interest along with the total number of events and non-events for each group defined by the CLASS variable. Which combination of statements and options should the analyst use?

Options :

A :

CLASS treatment / DESCENDING; MODEL event(binary) = treatment; OUTPUT OUT=pred_data PREDICTED=probabilities;

B :

CLASS treatment; MODEL event(event='1') = treatment / LINK=LOGIT; OUTPUT OUT=pred_data PREDICTED=probabilities;

C :

CLASS treatment; MODEL event(event='1') = treatment / LINK=LOGIT; OUTPUT OUT=pred_data PREDICTED=probabilities EVENTCOUNT=nwayevents NONEVENTCOUNT=nwaynonevents;

D :

CLASS treatment; MODEL event(binary) = treatment; OUTPUT OUT=pred_data PREDICTED=probabilities / NWAY;

Answer: C

Question 2

Which of the following link functions is appropriate when using the HP GLM node for a binary outcome variable in a logistic regression model?

Options :

A :

Identity link

B :

Log link

C :

Logit link

D :

Inverse link

Answer: C

Question 3

When preparing data for a predictive modeling project, a data scientist notices that the categorical variable 'payment_type' with four categories ('credit card', 'debit card', 'paypal', 'other') exhibits a high degree of variability in the outcome variable (purchase amount). To improve the model's predictive accuracy, what strategy can the data scientist use to handle the 'payment_type' variable?

Options :

A :

Group all categories into a single 'payment_type' indicator variable.

B :

Perform one-hot encoding on 'payment_type' to create a binary indicator for each category.

C :

Collapse 'paypal' and 'other' into a single category if they have similar effects on the purchase amount.

D :

Treat 'payment_type' as a continuous variable to simplify the model.

Answer: B

Question 4

A data scientist is using the SAS system to preprocess data for predictive modeling. They need to convert a character variable named `char_date` in the format 'yyyy-mm-dd' to a SAS date variable. Which INPUT statement below correctly performs this conversion?

Options :

A :

date_var = INPUT(char_date, yymmdd10.);

B :

date_var = INPUT(char_date, date9.);

C :

date_var = INPUT(char_date, yymmddd10.);

D :

date_var = INPUT(char_date, ddmmyy10.);

Answer: A

Question 5

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

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