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

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 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 correct syntax for the LIBNAME statement in SAS, which assigns a libref to a library stored at a specific path, requires the libref to come first, followed by the path enclosed in quotes. The format is libname libref 'path';. Thus, the correct syntax among the options provided is Option B: libname mydata 'c:\sas\data';. This statement assigns the libref 'mydata' to the directory 'c:\sas\data'. The other options either invert the order of the libref and path or improperly use the equals sign, which is not standard syntax for the LIBNAME statement.

References:SAS documentation on the LIBNAME statement, SAS Institute.

When defining custom formats in SAS, it's important to adhere to the correct syntax, which includes ending format names with a period. In the submitted program, the format $convert is defined without a period at the end of the format name in the VALUE statement. This is likely causing an error since format names in the VALUE statement should always end with a period. Option C correctly identifies that adding a period to the end of the format name on the VALUE statement will allow SAS to properly recognize and apply the custom format to the Answer variable when the PROC PRINT step is executed.

The program provided in the question seems to have formatting errors, but based on the information provided, the suggested change is to add a period to make it $convert. which would correctly apply the format.

The other options would not resolve the issue of applying the custom format:

• A. Changing the case of the unformatted values will not help if the format is not correctly specified.
• B. The comma does not seem to be the issue based on the context given.
• D. The dollar sign is correct and necessary for character formats; removing it would cause the format to be invalid for character data.

References:

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

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.

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 the given statement, BirthDate = 15DEC2005’d;, the 'd' denotes a SAS date constant, which is a numeric value representing a specific date. SAS date values are stored as numeric variables counting the number of days from January 1, 1960. Therefore, BirthDate is a numeric variable, making option B correct. This constant does not represent a character string (eliminating A), and the byte size of any numeric variable in SAS is 8 by default, not 9 (eliminating C). There is no syntax error in the statement (eliminating D).

References:

• SAS documentation on dates, times, and datetime values, 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.

SAS handles missing values distinctively based on the data type of the variable. For numeric variables, SAS represents missing values with a period (.). For character variables, missing values are represented by a blank space. This handling is crucial for distinguishing between actual data entries and absent data, particularly in statistical calculations and data processing. Options B, C, and D incorrectly describe the representation of missing values in SAS, which does not use special characters like 'N', 'C', or '$' for this purpose.

References:SAS documentation on handling missing data, SAS Institute.

In the provided code snippet, Line 3 contains a syntax error. The keep statement is used incorrectly here; the correct keyword to use is where in order to filter the dataset. The correct line should be something like:

where Make = 'Honda';

So, the keep statement on Line 3 is not properly used, and also there appears to be a missing quotation mark at the end of 'Honda' which should be closed before the semicolon.

References:

• SAS documentation for the where statement.

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 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 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

In SAS, the SUBSTR function is used to extract a substring from a character string. The function syntax is SUBSTR(string, position, length), where:

• string is the variable or string literal you want to extract the substring from.
• position is the starting position of the substring within string.
• position starts counting at 1 in SAS, not 0 as in some other languages.
• length is the number of characters to extract.

For the value of date provided ("02Apr2019"), we want to extract the year, which is the four characters at the end of the string.

Here's how each option would work given the string:

A) year=substr(date, 7, 4);This starts at the 7th character of the string ("2019") and extracts 4 characters, which correctly represents the year.

B) year=substr(date, 4, 6);This starts at the 4th character ("Apr2019") and would extract 6 characters, which gives us "Apr201", not just the year.

C) year=substr(date, 6, 4);This starts at the 6th character ("r2019") and would extract 4 characters, resulting in "r201", which is not correct.

D) year=substr(date, 4, 7);This starts at the 4th character and would extract 7 characters, resulting in "Apr2019", which is the whole string from the 4th character to the end, not just the year.

The correct answer is A, as it extracts the four-digit year from the end of the string.

References:

• The SAS documentation on the SUBSTR function, which details how to use it for extracting parts of strings.
• SAS programming tutorials that often provide examples of common functions like SUBSTR for string manipulation

The type of error that does not produce expected results and does not generate errors or warnings in the log is a logic error. Logic errors occur when there is a flaw in the program's logic, which causes it to operate incorrectly, but the syntax is correct so it does not produce any error or warning messages. Unlike syntax errors which are mistakes in the program's code that prevent it from compiling or running, logic errors are more insidious because the program still runs but yields incorrect results. For example, a programmer may accidentally code an incorrect formula or use a wrong variable name that still exists, so the program runs but produces incorrect output.

References:

• SAS documentation on error types.

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 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

When using a BY statement in a DATA step, SAS creates two temporary variables for each BY variable: FIRST.variable and LAST.variable. These are used to indicate the beginning and end of each BY group.

For Ellen's observation:

• First.State=1 because this is the first occurrence of the state "OH" in the dataset when sorted by the state variable, which makes it the beginning of a new group for "OH".
• Last.State=0 because Ellen is not the last observation

for the state "OH" group. In the given dataset snippet, there appears to be only one observation for the state "OH", but since the data is sorted by state, and we don't see another "OH" below Ellen, we can deduce that Ellen's observation is not the last "OH" observation in the entire dataset. If there were no more "OH" observations following Ellen's, then Last.State would be 1.

The FIRST.variable is set to 1 for the first observation in each BY group and 0 for all other observations in the group. Conversely, LAST.variable is set to 1 for the last observation in each BY group and 0 otherwise. Since we're assuming that the dataset is larger than the snippet shown, and the data continues beyond what is visible, we must assume there are more observations for "OH" unless indicated otherwise.

References

• "SAS 9.4 Language Reference: Concepts." SAS Institute Inc.
• "BY-Group Processing" in "SAS Programming 1: Essentials" course material, SAS Institute.

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 PROC SORT step in SAS is used to sort data sets by one or more variables. The syntax for the proc sort step requires the by statement to specify the variable(s) by which to sort the data. The correct answer is option A:

proc sort data=Names;

by Name;

run;

This sorts the data set by the variable 'Name' in alphabetical order, which matches the sorted NAMES data set shown in the question. Option B uses orderby, which is not a valid SAS statement. Option C sorts by 'Age', which would not give the same result as shown in the question. Option D also incorrectly uses orderby.