Practice A00-225 Exam Online

Question 1

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?

A : &EM;_DATA_INPUT

B : &EM;_IMPORT_DATA

C : &EM;_DATA_TABLE

D : &EM;_MACRO_VAR

Answer: A

Question 2

You have a decision tree with a depth of 4. During validation, you observe the following characteristics: Leaf nodes at the lower depths have higher confidence for the predicted class but represent a smaller portion of the dataset. Conversely, upper-level nodes cover more of the dataset but with lower confidence levels. Given this scenario, which of the following strategies might help in improving the model’s performance without overfitting?

A :

Increase the minimum number of instances required to split a node.

B :

Prune the tree to reduce its depth.

C :

Decrease the minimum number of instances required for a node to be considered a leaf.

D :

Allow the tree to grow deeper by increasing the maximum depth.

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?

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 built several predictive models using training data and now you want to assess the models' performance using validation data. Which measure is NOT appropriate for comparing model performance in terms of model bias for continuous outcomes?

A :

Root Mean Squared Error (RMSE)

B :

Akaike’s Information Criterion (AIC)

C :

Mean Absolute Error (MAE)

D :

Coefficient of Determination (R-squared)

Answer: B

Question 5

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?

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

Name:	SAS Advanced Predictive Modeling
Exam Code:	A00-225
Certification:	SAS Administration
Vendor:	SAS Institute
Total Questions:	347
Last Updated:	Apr 23, 2024

SAS Institute A00-225