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NEW QUESTION # 14
You have an Excel file 'customer data.xlsx' with two sheets: 'Customers' and 'Orders'. The 'Customers' sheet contains customer IDs and names, while the 'Orders' sheet contains order IDs, customer IDs, and order dates. You want to create a SAS dataset 'combined_data' that joins data from both sheets based on the common 'Customer ID' column. Which code snippet correctly accomplishes this?
Answer: D
Explanation:
Option B is the correct answer. It uses the MERGE statement to join the two datasets ('Customers' and 'Orders') based on the common column 'Customer_lD'. The BY clause specifies the join variable. Option A is incorrect because it uses the SET statement, which doesn't perform a join operation. Option C is incorrect as it simply concatenates the two datasets without a join condition. Option D is incorrect because it uses an IF statement and a subquery to select data from the 'Orders' dataset, which is inefficient Option E is incorrect as it simply combines the two datasets using SET, which doesn't join on a common column.
NEW QUESTION # 15
You are working with a large dataset containing information on customer transactions. You need to generate a report showing only transactions that occurred in the month of December and involved amounts greater than $1000. Which of the following WHERE statements would achieve this?
Answer: A,D
Explanation:
Option B uses the MONTH() function to extract the month from the TRANSACTION DATE, making it a robust approach- Option E provides a more explicit date range comparison. Option A is incorrect as it uses a string comparison for the date, which might not be reliable across different SAS versions or locales. Option C is incorrect as it specifies a specific year, while the requirement is for all December transactions. Option D is incorrect because the LIKE operator is used for string pattern matching, not date comparisons.
NEW QUESTION # 16
You have a CSV file named 'product_sales.csv' with the following structure: Product,Region,Sales,Date Apple,North, 100,2023-03-15 Orange,West, 150,2023-03-17 You need to import this data into a SAS dataset named 'ProductSales', but the 'Date' column should be in SAS date format (YYYYMMDD). Which code snippet correctly achieves this?
Answer: D
Explanation:
Option E is the correct answer. It uses the INFORMAT statement within PROC IMPORT to specify the input format for the 'Date' column as yymmdd8., which is the SAS date format for YYYYMMDD. Options A, B, and C do not specify any informat for the 'Date' column, leading to the data being imported in the original format. Option D uses the incorrect informat yymmdd10. which is for YYYY-MM-DD format. The correct informat for YYYYMMDD is yymmdd8.
NEW QUESTION # 17
You are working with a dataset containing information about employees, including their 'HireDate' (in SAS date format) and 'TerminationDate' (also in SAS date format). You need to create a new variable 'Tenure' that represents the number of days an employee worked for the company. However, you want to treat employees who are still active as having their 'TerminationDate' set to today's date. Which code snippet correctly calculates the tenure, considering active employees?
Answer: C,D
Explanation:
The correct answers are ''C'' and Here's why: 1. ''Handling Active We need to replace missing or empty 'TerminationDate' values with today's date to calculate tenure for active employees. 2- Function:'' Option C uses the 'coalesce()' function to replace missing values in 'TerminationDate' with 'today()' , effectively handling active employees. 3. ' Function:'' Option E uses the 'ifn()' function to check if ' TerminationDate' is missing, then it calculates the tenure based on today's date. If not missing, it uses the actual 'TerminationDate' . ''Why the other options are incorrect- A This option simply calculates tenure using the existing 'TerminationDate' which is incorrect for active employees. ' ''B & D:'' These options check for missing or empty values in 'TerminationDate' using the 'if missing()' or 'if TerminationDate = conditions, which are equivalent. However, they do not handle the situation when 'TerminationDate' has a specific date value that indicates the employee is still active (e.g., a very far-off future date)- The 'coalesce()' function in option C is better because it directly replaces missing values with 'today()' , making the code more concise and preventing unexpected results.
NEW QUESTION # 18
You have two SAS datasets, 'SALES' and 'CUSTOMERS', with a common key variable 'CUSTOMER ID. You need to merge these datasets, but you only want to keep records from 'SALES' that have a corresponding match in 'CUSTOMERS'. Which of the following code snippets correctly accomplishes this using the IN= option within a MERGE statement?
Answer: B
Explanation:
The correct answer is option A The IN= option within the MERGE statement creates variables that indicate whether a record was found in the corresponding dataset In this scenario, we want to keep only records from 'SALES' (sales_in = 1) that have a corresponding match in 'CUSTOMERS' (cust_in = 1)- Therefore, the condition 'if sales_in and cust_in' ensures only records with a match in both datasets are included in the final dataset.
NEW QUESTION # 19
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