Free and Premium SAS Institute A00-215 Dumps Questions Answers

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 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 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.

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

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.

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 reason the labels for MSRP, MPG_City, and MPG_Highway are not displaying in the PROC PRINT report is that the LABEL option was not used in the PROC PRINT statement. In SAS, even if you have label statements in your DATA step, you must also specify the LABEL option in the PROC PRINT statement for those labels to be used in the report output. Thus, the correct answer is Option A: You must use the LABEL option on the PROC PRINT statement.

References:

• SAS documentation on the PROC PRINT statement and LABEL option, 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.

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 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 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

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 code creates two datasets, preteen and teen, based on the age variable from the sashelp.class dataset. The if statement checks each observation, and if the age is less than 13, the observation is written to the preteen dataset; otherwise, the observation is written to the teen dataset.

From the provided frequency table, we can see that there are 7 observations with ages less than 13 (2 for age 11 and 5 for age 12), so the preteen dataset will have 7 observations. Since the sashelp.class dataset has a total of 19 observations, the remaining 12 observations (19 total - 7 preteen = 12 teen) will be written to the teen dataset.

Thus, the answer is: B. preteen will have 7 observations and teen will have 12 observations.

References:

• SAS documentation on the DATA step and IF-THEN/ELSE logic, SAS Institute.

In SAS, footnotes are set using the footnote statement and they will appear on all subsequent output until they are either changed or cleared. Based on the second image provided with the SAS code, the footnote for the second PROC PRINT report is set immediately before it runs.

The code sets footnote1 as 'Created by HR' and footnote2 as 'Confidential' initially. However, before the second PROC PRINT step, footnote2 is redefined as 'Draft - Do Not Distribute'. Since footnote1 is not redefined or cleared, it is no longer in effect for the second report.

Therefore, the only footnote that appears for the second PROC PRINT report is what is defined for footnote2 at that point in the code, which is 'Draft – Do Not Distribute'. That's why the correct answer is D.

References:

• SAS 9.4 documentation for the FOOTNOTE statement: SAS Help Center: FOOTNOTE Statement

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.

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

In the provided image of the SAS code for the PROC PRINT step, the following statements are present:

• proc print data=sashelp.cars; - PROC PRINT step beginning
• var Make Model MSRP MPG_City MPG_Highway Horsepower Weight; - VAR statement to specify variables to print
• format Weight comma8.; - FORMAT statement to apply a format to a variable
• The final run; statement which would be necessary to execute the PROC PRINT step is not visible in the image, but it can be inferred to be there since every PROC step must be ended with a run; or quit; statement.

Thus, there are four statements related to the PROC PRINT step.

References:

• SAS 9.4 Language Reference: Concepts, "PROC PRINT"
• SAS documentation on "VAR Statement" and "FORMAT Statement"

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.

To create SAS date values, the correct syntax involves specifying the date in a string format followed by the letter 'd'. This indicates to SAS that the string should be interpreted as a date value. Therefore, the correct answers are:

• A. "10/19/2019"d - This format directly specifies the month, day, and year, enclosed in quotes followed by 'd', which SAS correctly interprets as a date value.
• C. "19Oct2019"d - This format uses a three-letter abbreviation for the month combined with the day and year, also followed by 'd'. This is another valid way to specify a date in SAS.

Option B, mdy(10, 19, 2019), is incorrect because while the MDY function does create date values from month, day, and year components, the function itself should be used within a DATA step or another context where SAS functions are evaluated, and it doesn't directly create a date literal in the same way as options A and C.

Option D, mdy(19, Oct, 2019), is syntactically incorrect because the MDY function expects numeric arguments for the month, day, and year, and "Oct" as a literal month name is not a valid numeric argument.

References: SAS 9.4 Language Reference: Concepts.

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 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.

In the provided DATA step, the merge statement is used to combine the PATIENT and VISIT data sets by the variable Id. The number of observations in the resulting ALLVISITS data set will equal the number of unique Id values that appear in both the PATIENT and VISIT data sets, because the merge in SAS is a one-to-one merge by default when the by statement is used without additional options like in=.

Looking at the provided data sets, each Id in the PATIENT data set has corresponding entries in the VISIT data set. Since there are 5 unique Id values and the VISIT data set contains multiple observations for some Id values (specifically, Id 2 and 5 have more than one visit), the ALLVISITS data set will have a total of 7 observations (1 for each patient plus the additional visits for Id 2 and 5).

References:

• SAS documentation on the merge statement.