A00-215 Exam Dumps : SAS Certified Associate: Programming Fundamentals Using SAS 9.4

The report shown is a cross-tabulation of 'Region' by 'Product'. The FREQ Procedure indicates that PROC FREQ has been used, and the presence of 'Frequency', 'Percent', 'Row Percent', and 'Column Percent' suggests that a cross tabulation (or contingency table) has been requested. The correct code for this output would include the tables statement to specify the two variables ('Region' and 'Product') with an asterisk between them, which indicates a request for a cross-tabulation of these two categorical variables, and the crosslist option to display the table in a cross-tabulated list format. Therefore, option D is the correct answer:

proc freq data=sashelp.shoes;

tables region*product / crosslist;

run;

Options A and C use the order=freq option incorrectly, as this option would order the table by frequency counts rather than providing a cross-tabulation. Option B is missing the crosslist option needed to produce the cross-tabulated format.

To rename a variable when reading a dataset in SAS, you use the rename= option inside the parentheses following the dataset's name in the set statement. The correct syntax for renaming the variable 'Name' to 'StudentName' is: rename=(oldname=newname). Therefore, the correct option is A, which correctly places the old variable name 'Name' first, followed by an equal sign, and then the new variable name 'StudentName'. The parentheses enclose the rename operation, and it is correctly nested within the set statement.

References:

• SAS documentation on the RENAME= data set option, SAS Institute.

In the provided DATA step, the merge statement is used to combine the PATIENT and VISIT data sets by the variable Id. The number of observations in the resulting ALLVISITS data set will equal the number of unique Id values that appear in both the PATIENT and VISIT data sets, because the merge in SAS is a one-to-one merge by default when the by statement is used without additional options like in=.

Looking at the provided data sets, each Id in the PATIENT data set has corresponding entries in the VISIT data set. Since there are 5 unique Id values and the VISIT data set contains multiple observations for some Id values (specifically, Id 2 and 5 have more than one visit), the ALLVISITS data set will have a total of 7 observations (1 for each patient plus the additional visits for Id 2 and 5).

References:

• SAS documentation on the merge statement.