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

Option A is correct. The (obs=10) option is used as a data set option within the proc print statement to limit the number of observations processed to the first 10. The syntax should be placed immediately after the data set name within parentheses. Options B, C, and D are incorrect because they either use the wrong option or place the obs option incorrectly outside of the parentheses or with incorrect syntax.

References:

• SAS 9.4 documentation on the obs= data set option.

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.

In SAS, character variables are indeed capable of holding alphabetic characters, numeric digits, and other special characters, which makes Option A the correct answer. This flexibility allows for storing a wide range of data as text, including combinations that may include symbols and numbers typically found in addresses, identification codes, or textual data that includes special characters. Unlike numeric variables, character variables do not interpret the data as numbers but as literal strings of characters. Options B, C, and D contain inaccuracies regarding how data types and values are treated in SAS. Specifically, numeric values in SAS are never enclosed in quotes (which contradicts B), character variables can exceed 200 bytes depending on the specific declaration (contradicting C), and while numeric variables can indeed include numbers, decimal points, minus signs, and scientific notation, they do not typically include currency symbols as part of the variable's numeric value (contradicting D).

References:SAS documentation on data types, SAS Institute.

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.

The SAS program provided includes a PROC FORMAT step that defines a numeric format called Syn and applies this format to the variable answer in the PROC PRINT step. The format maps the value '0' to 'No', '1' to 'Yes', and all other values to 'Unknown'.

However, there is a syntax error in the PROC FORMAT step: it uses a dollar sign before Syn, which is used to indicate character formats, but the format is defined for numeric values (0 and 1). Therefore, when applying this format to the answer variable in PROC PRINT, it should not have a dollar sign, it should be format answer Syn.; instead of format answer $yn.;.

The correct output based on the given code, assuming the error with the dollar sign is corrected, would be as follows:

• For numeric values 0 and 1, 'No' and 'Yes' would be displayed, respectively.
• For any other numeric value, 'Unknown' would be displayed.
• For character values, the format would not apply and the actual values would be displayed.

Given that the values in the 'answer' column are numeric, the program will format them according to the defined Syn format. Thus, the report corresponding to Option D, which shows numeric values unformatted, would be the one created. This indicates that the format was not applied because of the syntax error with the dollar sign.

References:

• SAS 9.4 documentation for the FORMAT procedure and statement: SAS Help Center: PROC FORMAT

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 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.

The correct answer is B. The set statement in SAS can concatenate multiple datasets together. The AMERICAN and NATIONAL datasets appear to have the same structure and variables, so the set statement without any options will combine them into one dataset called BASEBALL. The order of the datasets in the set statement determines the order of the observations in the output dataset, and since there is no variable that needs renaming to match between the two datasets, option B is the correct answer.

References:

• SAS documentation on the set statement.

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.

In the PROC PRINT statement, the LABEL option is used to display variable labels in the report. If the variables in the dataset have labels associated with them, the LABEL option will ensure that these labels are used in the output instead of the variable names.

Here's how the LABEL option is used:

• proc print data=sashelp.class label; (assuming sashelp.class is the dataset in question)
• The LABEL keyword after the dataset name within the PROC PRINT call activates the use of variable labels in the output report.

The other options provided are incorrect for the following reasons:

• SHOWLABELS is not a valid SAS option.
• COLS does not exist as a PROC PRINT option in SAS.
• LABELS= is not a correct syntax for any SAS procedure.

References:

• SAS 9.4 documentation for the PROC PRINT statement with LABEL option: [SAS Help Center: PROC PRINT]

In SAS, the code you provided involves creating two datasets, table1 and table2, from the dataset sashelp.shoes. The key part to understand here is how the DATA statement and OUTPUT statement interact with the specified datasets.

• DATA Statement: The statement data table1 table2; initiates the creation of two new datasets named table1 and table2.
• SET Statement: The set sashelp.shoes; statement is used to read data from the sashelp.shoes dataset. This dataset includes data on shoe sales from SASHELP library, which is commonly used for demonstration purposes in SAS.
• OUTPUT Statement: In the context of the DATA step where multiple datasets are specified in the DATA statement (as in table1 table2), the OUTPUT statement without a dataset name specified outputs the current observation to all datasets listed in the DATA statement. This is a critical point because it determines where the data goes after processing in the DATA step.
• Execution: When the run; statement is executed, it processes each observation from sashelp.shoes. For each observation, because there is no condition or additional OUTPUT statements specifying dataset names, each observation is output to both table1 and table2.

Therefore, the correct behavior as described is that each observation in sashelp.shoes is written to both table1 and table2. This effectively duplicates each row from the source into both target datasets.

References:

• SAS 9.4 Language Reference: Concepts, "DATA Step Processing" and "OUTPUT Statement" sections provide detailed explanations on how DATA steps process and how OUTPUT statement works in different contexts.
• Practical examples and explanations from SAS programming courses and official SAS documentation, which discuss DATA and SET statements, and their interaction with OUTPUT in data duplication scenarios.

The correct answer is C. The DATA step and PROC PRINT steps ran without errors. In the SAS log shown, there is a clear error in the PROC SORT step because of the use of an incorrect option by ascending Increase which caused the "Variable ASCENDING not found" error. However, the DATA step completed successfully, as indicated by the "NOTE" that follows it, confirming that 56 observations were read from the SASHELP.SHOES data set where Region is Africa. Additionally, the PROC PRINT step also completed successfully, indicated by the "NOTE" at the bottom of the log which confirms that 56 observations were read from the WORK.AFRICA data set, implying the PROC PRINT step executed without issue.

A is incorrect because the error in PROC SORT did not cause the program to stop; SAS continued processing the next steps. B is incorrect because the PROC SORT step did not run successfully. D is incorrect because the PROC PRINT step did not fail; it ran successfully.

References:

• SAS documentation on error messages.
• Understanding the SAS log and error messages.

The correct answer is B: The XLSX engine can read and write data in Microsoft Excel workbooks. This functionality allows SAS users to directly access and manipulate data stored in Excel files using the .xlsx extension. The XLSX engine does not automatically concatenate individual worksheets; instead, each worksheet is accessed separately. Also, it specifically works with files that have the .xlsx extension, not the older .xls format, thus eliminating option C. Option D is incorrect because the XLSX engine requires the correct file extension (.xlsx) in the LIBNAME statement to properly identify and interact with Excel files.References:

• SAS documentation on LIBNAME statement for XLSX engine: SAS Support: LIBNAME Statement

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 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 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.

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 FORCAST dataset is created from the INVEST dataset, which has 10 observations as seen in the PROC PRINT report. For each observation in INVEST, three new observations are added in FORCAST, one for each year's balance calculation (year=1, year=2, year=3). This is achieved by the output; statement after each balance calculation, which writes the current state of the data step to a new row in the dataset. Therefore, since there are 10 original observations, and each leads to three new observations, the total number of observations in the FORCAST dataset will be 30 (10 observations from INVEST multiplied by 3 years each).

References:

• SAS documentation on the DATA step and output statement, SAS Institute.

In SAS, iterative DO statements are used to repeat a block of statements a specified number of times. These DO statements have a specific syntax that includes initializing a variable, setting an ending value, and specifying an increment (or decrement). The syntax generally follows the pattern: DO variable = start TO end BY increment;

Let’s evaluate each option provided:

A) Do 100 to 1200 by 100;This statement is syntactically incorrect because it lacks a variable to iterate over. An iterative DO loop must specify a variable that will take on each value in the specified range. The correct form should be something like do i = 100 to 1200 by 100;.

B) Do num = 1.1 to 1.9 by 0.1;This statement is valid. It initializes the variable num at 1.1 and increments by 0.1 until it reaches 1.9. This is a typical use of the iterative DO loop for non-integer increments.

C) Do year = 2000 to 2016 by 2;This statement is also valid. It initializes year at 2000 and increments by 2, going through values like 2002, 2004, etc., up to and including 2016. This is a standard use for iterating over years or other sequentially numbered items.

D) Do reverse = 10 to 1 by -1;This statement is valid. It initializes reverse at 10 and decrements by 1 until it reaches 1. Using negative increments is a legitimate approach for counting downwards.

References

• SAS 9.4 Statements: Reference, "DO Statement."
• SAS Support: DO Loop Documentation

Understanding how to properly format DO loops in SAS is fundamental for effectively managing repetitive tasks in your data analysis. Mistakes like those seen in option A can lead to syntax errors, preventing your code from executing. Always ensure that your loops are correctly structured and that each component of the loop is clearly defined.

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"

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.