SAS Institute A00-215 SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Exam Practice Test

In the given DATA step, the do weeks=1 to 3; loop iterates three times for each row in the original WORK.RUN dataset, incrementing the miles value by 5 for each iteration and outputting a row after each increment. However, no output; statement is given within the loop to create the extra rows, which means only the last iteration of the loop will be outputted for each original row. Hence, the miles value for each name will be incremented by 15 (5 miles for each of the 3 weeks).

The original data set WORK.RUN has:

John 16

Mary 20

After the DATA step, it should be:

John 31

Mary 35

Each name appears only once because without an explicit output; statement inside the loop, only the final iteration is outputted. The variable weeks is not retained in the output dataset, as it is not used outside the loop and no output statement is used inside the loop. Thus, option D is correct.

References:

• SAS documentation on DATA step processing, SAS Institute.

The error messages in the SAS log indicate issues with the treatment of text as a character string. In SAS, character strings must be enclosed in quotation marks. The log shows that 'ABC Inc.' is treated as if it is a variable or an expression, which is causing a syntax error. Enclosing 'ABC Inc.' in quotation marks will correctly identify it as a character string. Therefore, Option B is the correct action to resolve the error messages. This error is evident as the log indicates it was expecting an arithmetic operator, which suggests that the text was misinterpreted as part of an expression.

References:

• SAS documentation on character strings and data step options, SAS Institute.

The correct syntax for the CONTENTS procedure is to specify the dataset to be described using the data= option. The dataset should be identified by its library and name, separated by a period. The correct syntax is therefore Proc contents data=library.dataset;, which makes Option C the correct answer: Proc contents data=sashelp.shoes;. This statement will produce the contents of the dataset 'shoes' in the 'sashelp' library.

References:

• SAS documentation on the CONTENTS procedure, SAS Institute.

The correct answer is option D, which uses the LIBNAME statement correctly to assign a library reference to an Excel workbook and specifies the correct syntax for accessing a worksheet within that workbook. The syntax for this option is:

libname exlib xlsx "c:\class.xlsx";

proc print data=exlib.class_test;

run;

This code snippet assigns the library reference exlib to the Excel workbook located at c:\class.xlsx using the XLSX engine. The PROC PRINT step is then used to display the contents of the worksheet named CLASS_TEST within that workbook. The library reference exlib combined with the dataset name class_test (following the .) correctly specifies the worksheet to be printed. The other options (A, B, C) are incorrect due to various reasons: incorrect syntax for the LIBNAME statement, incorrect specification of the dataset to be printed, and incorrect path specifications.References: SAS 9.4 Guide to Software Updates.

The step that reads the SASHELP.BASEBALL data set and creates the temporary data set CATCHERS is:

data catchers;

set sashelp.baseball;

where position='C';

run;

This DATA step creates a new data set named catchers by reading in data from sashelp.baseball and selecting only those observations where the variable position has the value 'C', which stands for catchers.

The correct statement to import a CSV file into SAS and create a dataset is option B. The PROC IMPORT statement is used in SAS to read data from external files and create a SAS data set. Let's break down why option B is correct:

• datafile= specifies the location and filename of the external data file. In this case, "sales.csv" is the correct filename and format for a CSV file.
• dbms=csv tells SAS that the format of the external data file is CSV (comma-separated values).
• out=mydata.sales; specifies the name of the output SAS dataset. It consists of two parts: the libref mydata and the dataset name sales. This tells SAS to store the new dataset in a library called mydata with the dataset name sales.

Option A uses an incorrect syntax as it incorrectly specifies the data= option, which is not valid in this context. Also, the out= option is incorrectly quoted and terminated with a semicolon within the quotes.

Option C has a typo in the out= option (out=mydata.gales;), which incorrectly specifies the output dataset name. Additionally, it incorrectly specifies the data= option, which should actually be datafile=.

Option D has incorrectly quoted the out= option and uses a hyphen instead of an equals sign. Additionally, it does not use quotes around the filename, which may cause issues if the filename contains special characters or spaces.

References:SAS 9.4 documentation, specifically the PROC IMPORT statement: SAS Help Center: PROC IMPORT Statement

The correct answer is A. The SET statement in SAS is used within a DATA step to specify which existing SAS dataset is to be read. This statement is used to read in data from an existing SAS dataset to be processed in the current DATA step. The SET statement does not specify an output data set; that is determined by the name specified in the DATA statement.

Options B, C, and D are incorrect because:

• B and C are incorrect as the SET statement is not used in the PROC SORT step at all.
• D is incorrect because the SET statement specifies the input dataset, not the output dataset, in the DATA step.

References:

• SAS 9.4 Language Reference: Concepts, "DATA Step Processing"
• SAS documentation on the SET statement.

The VAR statement in PROC MEANS is used to specify the variables for which you want to calculate the descriptive statistics such as the mean and standard deviation. You list the names of one or more numeric variables after the VAR statement to include them in the analysis. Therefore, option D is the correct answer.

References

• SAS documentation for PROC MEANS.

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 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 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.

Option B is the correct answer. This code will create two datasets as described. The keep= option in the data chips statement will ensure that only QtySold, Price, and Product are kept in the chips dataset, and because othersnacks does not have a keep= option, it will contain all the variables from the input dataset. The if-then-else logic directs the observations to the correct dataset based on the Type value.

Option A incorrectly uses a keep statement within the if block, which is not valid syntax. Option C attempts to use a keep statement after the output statement, which will not correctly subset the variables for the chips data set. Option D incorrectly applies the keep= option to the set statement, which would affect both output data sets, not just chips.

References:

• SAS documentation on the output statement.
• SAS documentation on subsetting data in the DATA step.

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 program fails because option C is correct: you must include the dataset height2 in the DATA statement. The DATA step is trying to write to a dataset named height2, which has not been defined in the DATA statement. The correct syntax to create two datasets in one DATA step is to list them both in the DATA statement like so:

data height1 height2;

The use of numbers in dataset names is not a problem in SAS, nor is outputting to different data sets within a single DATA step. Additionally, using two different DATA statements for HEIGHT1 and HEIGHT2 is not required and is not the cause of the failure.

References:

• SAS documentation on the DATA statement and creating multiple data sets.

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.

Option C is correct. The set statement is used within a DATA step to read an existing SAS data set. In this case, set sashelp.shoes; reads the SASHELP.SHOES data set, and data work.total; specifies the creation of a new data set named TOTAL in the WORK library. The options A, B, and D are incorrect because they either use the wrong syntax or are not proper statements for reading an input data set and creating an output data set.

References:

• SAS 9.4 documentation on the DATA step.

In SAS, the PROC SORT procedure is used to sort data. To save the sorted data into a new dataset, the OUT= option is used in the PROC SORT statement. This option allows you to specify the name of the output dataset that will contain the sorted data.

Here is how it's used:

• proc sort data=original out=sorted; by variable; run; will sort the dataset original by variable and create a new dataset named sorted containing the sorted data.

Option A, Data=, is not valid for the PROC SORT procedure. Option B, SORTOUT=, is a common misconception but is not correct; OUT= is the right option. Option C, OUTPUT=, is not used within PROC SORT; it is used in other procedures such as PROC TABULATE and PROC SUMMARY for output datasets.

References:

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

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.

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 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

To subset data in SAS using the data step, the where statement is used to specify the conditions that observations must meet to be included in the new data set. The correct syntax for subsetting the SASHELP.BASEBALL data set to include only players in the 'East' Division with 75 or more hits is as follows:

data bball;

set sashelp.baseball;

where Division = 'East' and nHits >= 75;

run;

This code, as shown in option D, uses the where statement with the correct logical operator and to ensure that both conditions must be true for a record to be included in the new dataset. Options A, B, and C either use incorrect syntax or logical operations that do not match the required conditions for the subset. Option A has an incorrect combination of conditions, B uses two where statements which is not valid syntax, and C incorrectly uses the or operator which would include players not in the 'East' Division or with fewer than 75 hits.

The correct syntax for generating the mean and standard deviation for specified variables using PROC MEANS is shown in option A. The PROC MEANS statement specifies the dataset to analyze (data=class) and includes the options (mean std) directly in the PROC statement. The VAR statement then lists the variables for which the statistics should be calculated (Height and Weight). The other options listed in B, C, and D are not correct syntax for PROC MEANS.