SAS Institute A00-215 Dumps

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 answer is B. The DATA step in SAS is used to read, process, and create data sets. It can read from various sources, not just raw data files but also existing SAS datasets and external databases. It processes the data by executing operations on each observation and can create one or more SAS datasets as output. The other options are incorrect because:

• A is incorrect because the DATA step is not limited to reading only raw data files.
• C is incorrect because the DATA step can output multiple data sets using the OUTPUT statement.
• D is incorrect because the DATA step does not have to be the first step in a program; it can be used anywhere in the program where data processing is required.

References:

• SAS 9.4 Language Reference: Concepts, "DATA Step"
• SAS documentation on processing data with the DATA step.

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 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.

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 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"

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.

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.

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

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.

In SAS, the FORMAT procedure (PROC FORMAT) allows users to create custom formats that can be used to change the way data is displayed without altering the data itself. The VALUE statement within PROC FORMAT is a key component of this procedure.

The VALUE statement can indeed create both numeric and character formats, which is why option C is correct. Numeric format names typically do not start with a $ sign (this is reserved for character format names), and the keyword UNKNOWN is not a part of the VALUE statement syntax; it is used in a different context within PROC FORMAT, specifically for handling undefined data values when using custom formats.

Moreover, the LIB= option is not used within the VALUE statement. The LIB= option is used at the PROC FORMAT level to specify the library where the formats should be stored or from which they should be read.

References

• SAS 9.4 documentation for the PROC FORMAT and VALUE statement.
• "SAS Formats and Informats." in "Step-by-Step Programming with Base SAS 9.4" from the 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]

The FORMAT statement in the DATA step assigns formats to variables for the SAS dataset being created. In the code provided, MSRP is given the dollar12. format and Invoice is given the dollar10. format. However, during the PROC PRINT step, MSRP is reassigned to the comma12.2 format. The format specified in the most recent PROC step or DATA step that executes is the one that is used in the output.

Therefore, the format for MSRP in the PROC PRINT output will be comma12.2, making the answer: B. Comma12.2

References:

• SAS documentation on PROC PRINT and FORMAT statement, SAS Institute.

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.

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 PROC CONTENTS step in SAS is used to display metadata about a SAS dataset, including information such as the names of variables, types of variables (numeric or character), lengths of variables, and other attributes like formats, labels, and indexes. Therefore, the correct answer is:

• A) a report showing the descriptor portion of the REVENUE data set

The descriptor portion of a SAS data set contains metadata about the dataset's structure, not the actual data values or any specific information about individual variables beyond their structure and definition in the dataset. Options B, C, and D suggest details that are not part of the dataset's descriptor information as shown by PROC CONTENTS.

References: SAS 9.4 Procedures Guide: Statistical Procedures.

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.

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.

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.

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 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 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