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SASInstitute SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which of the following SAS statements can be used to define a global macro variable that is accessible throughout the entire SAS session?

A. data _ null_; global_var = 'value'; run;
B. options global_var='value';
C. %let global_var = 'value',
D. %macro global_var; 0/01et global_var = 'value', %mend,
E. %global global_var = 'value',

Answer: E

Explanation:
The %global statement is specifically designed to declare global macro variables. Option A defines a local macro variable, accessible only within the current program or macro. Option C assigns a value to a regular SAS variable, which is not a macro variable. Option D creates a macro named 'global_var' and assigns the value to a local macro variable inside that macro. Option E is used to set global options, not to define macro variables.

NEW QUESTION # 29
You have a dataset with a variable named 'PRODUCT NAME' that contains long and descriptive product names. You want to modify the dataset by shortening the product names and creating a new variable called 'SHORT NAME'. The 'SHORT NAME' should be a maximum of 20 characters long, and the remaining characters should be replaced with '...'. Which of the following code segments will achieve this transformation?

Answer: C,E

Explanation:
Both Option B and Option E correctly achieve the transformation. They both use the SUBSTR function to extract the first 20 characters of the product name and append if the original name is longer than 20 characters- The 'else' statement handles the case when the product name is already shorter than 20 characters, copying the original name to the new variable. Option A is incorrect because it always truncates the product name to 20 characters and appends ' even if the original name is shorter than 20 characters. Option C is incorrect because it uses the SCAN function to extract the first word of the product name, not the first 20 characters. Option D is incorrect because it only limits the length of the new variable but does not truncate the product name and append '...' if it exceeds the limit. Therefore, only Options B and E correctly handle the transformation of product names to a maximum of 20 characters with ellipsis added when necessary.

NEW QUESTION # 30
Which SAS format displays a SAS date as 25JUN2019?

A. Date9.
B. ddMMMyy9.
C. Dmy9.
D. Ddmmmyyyy9.

Answer: A

Explanation:
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.

NEW QUESTION # 31
You have a SAS dataset called 'SALES' with variables 'Region', 'Product', 'Sales', 'Quantity', and 'Date'. You want to create a new dataset called 'Top_Sales' containing only the 'Region', 'Product', and 'Sales' variables for the top 10 regions with the highest total sales. Which code snippet would achieve this correctly?

Answer: E

Explanation:
Option E is the correct solution. It first sorts the dataset 'SALES' by the total sales in descending order. Then, it creates a new dataset 'Top_Sales', using the sorted dataset 'SALES_Sorted', and outputs only the first 10 observations. Finally, it uses the 'keep' statement to select the desired variables 'Region', 'Product', and 'Sales'. Option A outputs only the first 10 observations without considering the total sales by region. Option B uses SQL to group by region and filter based on a condition related to the average sales, which is not what the requirement asked for. Option C is incorrect because it uses 'obs=10' which will limit the dataset to 10 observations, regardless of total sales. Option D uses 'drop' to remove the unwanted variables, but it does not sort by sales or limit the dataset to the top 10 regions.

NEW QUESTION # 32
You have a dataset 'Transactions' with a variable 'TransactionDate' in a DATE format. You need to create a new variable 'DaysSinceLastTransaction' that calculates the number of days since the previous transaction for each record. Which SAS code snippet would correctly achieve this?

Answer: C

Explanation:
Option A is the correct code snippet to calculate the number of days since the previous transaction. It uses the 'lag' function to access the value of the 'TransactionDate' variable from the previous record and then calculates the difference in days between the current and previous 'TransactionDate' using 'intck('day' function. Option B is incorrect because it tries to subtract dates directly which is not valid_ Option C is incorrect because it calculates the difference in days between the current and previous 'TransactionDate' in the reverse order. Option D is incorrect because it calculates the difference between the current 'TransactionDate' and todays date. Option E is incorrect because it uses 'lag" function correctly but then it incorrectly sets the value of 'DaysSinceLastTransaction' to 0 if it is negative. The calculation of 'DaysSinceLastTransaction' should remain negative if the current 'TransactionDate' is before the previous 'TransactionDate'.

NEW QUESTION # 33
......

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