A00-215 SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Question and Answers

The correct syntax for a conditional if-then block in SAS that includes a do group for executing multiple statements is:

if condition then do;

statement1;

statement2;

end;

In Option B, the syntax if age<13 then do; opens a do group, and age_Cat='Pre-teen'; and account='No Social Media'; are the two statements inside the do group, followed by an end; statement to close the group. However, there is a typo with the use of dashes instead of equal signs for assignment in the variable statements which seems to be an error, so technically, none of the provided options has the correct syntax without an error. The correct syntax should use equal signs (=) for assignment within the do group:

if age<13 then do;

age_Cat='Pre-teen';

account='No Social Media Allowed';

end;

References:

• SAS documentation on conditional processing with IF-THEN/ELSE statements, SAS Institute.

Option B is correct. In SAS, variable names must be 1 to 32 characters in length. The other statements about variable names are incorrect:

• A is incorrect because variable names are not case sensitive in SAS.
• C is incorrect because variable names are required when you create a data set; each column must have a unique name.
• D is incorrect because variable names cannot start with a number, though they can start with an underscore or a letter.

References:

• SAS 9.4 documentation on SAS variables.

The SUM statement in SAS is used to sum values across observations, adding the value of the expression on the right side of the statement to the variable on the left. It does not use an equal sign (eliminating A), only one variable can be on the left-hand side of a SUM statement (eliminating B), and it is not initialized to 1 by default (eliminating D). What makes the SUM statement particularly useful is that it ignores missing values when summing across observations, which means that missing values do not affect the sum (option C is correct).

References:

• SAS documentation on the SUM statement, SAS Institute.

Data set options in SAS, such as KEEP= (to specify variables to keep in a dataset) and RENAME= (to change the names of variables in a dataset), have specific rules for their usage:

• A. The options must be placed in parentheses - This is correct. When specifying dataset options in SAS, they must be enclosed in parentheses immediately following the dataset name, without any spaces in between.
• D. The options are placed after the data set name - This is also correct. Dataset options follow the dataset name and are enclosed in parentheses to modify how SAS reads or writes the dataset in that step.

Option B is incorrect because data set options are allowed in both DATA and PROC steps, provided they are relevant to the procedure being used. Option C is incorrect because data set options can be applied to both input and output datasets, depending on the context and specific operation being performed.

References: SAS 9.4 Language Reference: Concepts.

In PROC PRINT, the VAR statement is used to control the order of the variables displayed in the report. You can list the variables in the order you want them to appear. The KEEP statement can control which variables appear, but not their order. DROP and SELECT are not valid statements within PROC PRINT for controlling the order of variables.

References

• SAS documentation for PROC PRINT.

The DROP statement during the compilation phase of the DATA step flags variables in the Program Data Vector (PDV) to be dropped when the data set is output. This means that although the variables exist in the PDV during the DATA step execution, they will not be written to the output data set. The DROP statement does not affect the order of variables (which is the purpose of the ORDER= data set option), nor does it remove variables from the input data set or prevent them from being created in the PDV.

References:

• SAS documentation on the DROP statement.

In the DATA step provided, average is calculated using the mean function, which calculates the average of the non-missing values of the variables listed. The variables provided are:

• var1 = 2
• var2 = 4
• var3 = .
• var4 = 6

The mean function in SAS ignores missing values, represented by a period (.). Hence, the average is computed using the non-missing values (2, 4, and 6). The mean of these three numbers is calculated as: (2 + 4 + 6) / 3 = 12 / 3 = 4

Therefore, the value of average is 4.

References:

• SAS documentation on the MEAN function, SAS Institute.

The PROC SORT statement that specifies the sort variable is: BY

The BY statement in PROC SORT is used to specify the variable(s) by which the data should be sorted.

The PROC MEANS statement is used to compute descriptive statistics of data in SAS. Option A is the correct code to produce the report shown in the first image because of the following reasons:

• data=sashelp.shoes specifies the dataset on which the procedure is to be performed.
• sum mean specifies that the summary statistics should include the sum and mean of the variables.
• var Sales; specifies that the variable Sales is the analysis variable for which the summary statistics are to be computed.
• class Product; specifies that the procedure should classify results by unique values of the Product variable. This will produce separate statistics for each type of product, which aligns with the structure of the report provided in the image.

Options B, C, and D are incorrect for the following reasons:

• B uses group instead of class, and group is not a valid statement in the context of PROC MEANS. Also, var Sale; is incorrect as the variable name is Sales.
• C includes nobe; which is not a valid SAS option and seems to be a typo. The by statement is used for sorting data, not for classifying groups as class does.
• D incorrectly uses sum Salad; and mean Sales; as separate statements and has an invalid use of by product; which is not needed here.

References:

• SAS 9.4 documentation for the PROC MEANS statement: SAS Help Center: PROC MEANS

The default length of numeric variables in SAS data sets is 8 bytes.

In SAS, the default byte size of a numeric variable is 8 bytes. This allows for a significant amount of precision and a wide range of numeric values to be stored within a numeric variable, regardless of the actual number of digits in the number being stored.

References:

• SAS documentation on numeric variables, SAS Institute.

The type and length of SnackType are character and 7, which makes option B correct. In the provided program, SnackType is assigned a value of "Chips" which is a character string. The length statement is used to explicitly declare SnackType as a character variable with a length of 7 before it is used, which will take precedence over any later assignments or implications about the variable's type or length.

References:

• SAS documentation on variable length.

The correct syntax for creating an Excel workbook using the ODS (Output Delivery System) Excel destination with a specific style is provided by option D. The syntax is as follows: ods excel file='c:\report.xlsx' style=analysis;This statement instructs SAS to output the results of subsequent procedures to an Excel workbook located at c:\report.xlsx, applying the ANALYSIS style to the output. The file= option specifies the path and name of the Excel file to be created or overwritten, and the style= option indicates the style template to be used for the output. The ANALYSIS style is one of the predefined styles that can be applied to the output to control the appearance of tables and graphs. Other options like A) and B) are incorrect due to syntax errors and misplacement of keywords. Option C) has a typo in the file path and incorrect syntax usage.References: SAS 9.4 Output Delivery System: User's Guide.

In SAS, when you use a BY statement in a DATA step, SAS creates two temporary variables for each variable listed in the BY statement: one to indicate the first occurrence (FIRST.variable) and another to indicate the last occurrence (LAST.variable) of the value within the BY-group. Given the provided code and assuming that State is the variable used in the BY statement, SAS will create First.State and Last.State. Thus, option A is correct.

References:

• SAS documentation on the BY statement, SAS Institute.

The resulting WORK.REVENUE data set contains 3 variables. In the provided SAS program, the set statement uses a (keep=) option to keep only 3 variables from WORK.PRODUCTS, which are ProdId, Price, and Sales. The drop= data set option in the data statement specifies to drop the Sales and Returns variables from the output data set. However, Returns was not included in the keep= option, so it won't be part of WORK.REVENUE to begin with. Finally, a new variable Revenue is calculated and included in the data set. Therefore, the final data set contains the variables ProdId, Price, and Revenue.

References:

• SAS documentation on keep= and drop= data set options.

In the provided program, there are six distinct SAS statements:

• data FemaleStudents; - Data step beginning
• set sashelp.Class; - Set statement
• where Sex="F"; - Where statement
• Classroom="Red Room"; - Assignment statement
• run; - Run statement to execute the data step
• title "Female Students in Red Room"; - Title statement

Note that the proc print and the second run; statement are part of another PROC step to print the results and hence are not counted in this particular count. The final title; statement is used to clear the title setting and does not count as part of the program statements being asked about.

References:

• SAS 9.4 Language Reference: Concepts, "DATA Step"
• SAS documentation on "TITLE Statement"

The PROC PRINT statement is used for printing SAS datasets. To customize the column headers in the report, you use the label statement within the PROC PRINT procedure to assign new labels to variables, and the label option in the PROC PRINT to activate these labels. The noobs option suppresses the printing of observation numbers in the report.

Option B has the correct syntax for what is being requested:

• proc print data=cities label noobs; specifies the dataset to print with the label option to display variable labels and noobs to hide the observation numbers.
• label Name='Employee Name' City='Birth City'; assigns the label 'Employee Name' to the 'Name' variable and 'Birth City' to the 'City' variable.
• run; signifies the end of the PROC PRINT procedure.

Options A, C, and D are incorrect due to syntax errors:

• A uses an incorrect option showlabelse instead of label and noobs.
• C is incorrect because it lacks the label option in the proc print statement which is necessary to actually display the labels, and the labels are being incorrectly used within the proc print instead of in a label statement.
• D has several issues: there is no display statement in SAS, the option noobs should be outside of the proc print statement, and the labels should be defined in a label statement, not within proc print.

References:

• SAS 9.4 documentation for the PROC PRINT statement: SAS Help Center: PROC PRINT

The report shown in the image is a two-way frequency table generated by the PROC FREQ procedure which shows the frequency and percentage distribution of two categorical variables. The correct answer is option B:

• proc freq data=cars; tells SAS to use the PROC FREQ procedure on the dataset cars.
• tables make*drivetrain; requests the frequency table for the cross of make and drivetrain. The asterisk (*) denotes a two-way table of the two categorical variables.
• run; indicates the end of the proc freq step.

The resulting table in the output has the statistics for Make broken down by Drivetrain category, which matches the structure seen in the provided image.

Options A, C, and D do not correctly request the two-way table of make by drivetrain. Specifically, C and D reverse the order, which would change the orientation of the table in the output, and A is missing the asterisk (*) which is needed for the cross-tabulation of the two variables.

References:

• SAS 9.4 documentation for the PROC FREQ statement: SAS Help Center: PROC FREQ

The correct answer is C. To create the NODONATIONS dataset, which includes only the employees who did not make any donations, a merge operation is used with a conditional statement to filter out those records. The syntax if inE=1 and inD=0; ensures that the merged dataset includes only the observations from the EMPLOYEES dataset that do not have a corresponding observation in the DONATIONS dataset. This is achieved using data step processing with the in= option to create temporary variables that indicate whether the data is coming from the input dataset.

In the other options:

• A is incorrect because inE=0 and inD=0 will never be true after a merge since at least one of the datasets must contribute to the merged observation.
• B lacks the necessary conditional logic to filter out employees with donations.
• D is incorrect because if inE=1 and inD=1; would actually select employees who made donations, the opposite of what we want.

References:

• SAS documentation on merging data sets using the DATA step.

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.

https://v8doc.sas.com/sashtml/lrcon/z0961108.htm

In SAS, the automatic variable _N_ represents the number of times the DATA step has iterated. It starts at 1 and increments by 1 each time the DATA step loops back to the top. This variable can be used within the DATA step to monitor the iteration count but is not written to the output data set. The other options, A, C, and D, are not automatically created variables that track iterations in the DATA step.

References:

• SAS documentation on automatic variables.