A00-215 Exam Dumps - SAS Certified Associate: Programming Fundamentals Using SAS 9.4

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 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 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.

https://v8doc.sas.com/sashtml/lrcon/z0961108.htm

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 answer is A: Ods excel='c:\report.xlsx' style=analysis;. This syntax is correct for the ODS EXCEL statement in SAS, where you specify the path and filename, followed by the 'style' option to apply a specific style template to the output Excel file. The 'style=analysis' is used to set the output appearance according to the 'ANALYSIS' style template provided by SAS. Option B is syntactically incorrect and does not use the 'style' option correctly. Option C incorrectly places a slash which is not syntactically valid in this context, and Option D misses the equals sign and quotes which are necessary for correct syntax in SAS.References:

• SAS documentation on ODS EXCEL statement: SAS Support: ODS EXCEL Statement

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 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

In SAS program syntax, character strings are indeed case sensitive when enclosed in quotation marks. If you compare two character strings of the same letters but different cases, SAS will treat them as different values. For example, 'Hello' is not the same as 'hello'.

Option A is incorrect because the ampersand (&) is not used for comments in SAS; it is used for macro variable references. Option B is incorrect because global statements such as LIBNAME and options do not require a RUN statement to execute. Finally, option D is incorrect because SAS can process steps with multiple statements on the same line, provided that they are correctly separated by semicolons.

References

• SAS 9.4 Language Reference: Concepts, "SAS Language Elements and Syntax."

Option B is correct. In SAS, variable names must be 1 to 32 characters in length. The other statements about variable names are incorrect:

• A is incorrect because variable names are not case sensitive in SAS.
• C is incorrect because variable names are required when you create a data set; each column must have a unique name.
• D is incorrect because variable names cannot start with a number, though they can start with an underscore or a letter.

References:

• SAS 9.4 documentation on SAS variables.

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 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 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.

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.

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, 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, 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 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.

When creating two SAS data sets with a DATA step, you should name both data sets in the DATA statement. This tells SAS to create two separate data sets from the same DATA step. Using an OUT= option in the WHERE statement or a PUT statement are not correct methods for creating data sets. A SET statement is used to read data into the DATA step, not to create output data sets.

References:

• SAS documentation on the DATA step for creating multiple data sets.

Option B is correct. The DATE9. format in SAS displays dates in the ddMMMyyyy format, which corresponds to the example given (25JUN2019). This format writes dates with a two-digit day, a three-character month abbreviation, and a four-digit year. The other options do not match the correct format:

• A is incorrect because ddMMMyy9. format would display a two-digit year.
• C is incorrect because there is no such format as Ddmmmyyyy9. in standard SAS formats.
• D is incorrect because Dmy9. does not correspond to the required format.

References:

• SAS 9.4 documentation on date and time formats.

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.