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Title: Guaranteed High Marks with Updated & Real A00-215 Dumps pdf Free Updates [Q224-Q244]

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 Guaranteed High Marks with Updated & Real A00-215 Dumps pdf Free Updates
PASS RATE Programming Fundamentals A00-215 Certified Exam DUMP


NEW QUESTION 224You are working with a dataset containing customer names. Some names are longer than 20 characters. You need to truncate these names to a maximum of 20 characters. Which of the following code snippets correctly uses the LENGTH statement to achieve this within a DATA step? ‘ ‘Note’ ‘: Assume the variable ‘CustomerName’ is a character variable.
 
 
 
 
 
The correct answers are and Option A defines the length of the variable ‘CustomerName’ to be 20 bytes and uses the ‘substr- function to truncate the name if it exceeds 20 characters. Option D also defines the length to be 20 bytes using ‘length CustomerName $ (using dollar sign to define character variables) and uses the ‘substr’ function to truncate the name. Option B uses the wrong syntax for defining character variable length. Option C attempts to use the ‘scan’ function to truncate the name, but it would only work if the name has spaces and would not truncate it correctly. Option E truncates the name but adds’ ‘ at the end of names longer than 20 characters. While this might be useful, it is not the most straightforward approach for simply truncating names to a specific length.NEW QUESTION 225You have a CSV file named ‘customer_data.csv’ containing customer information. You need to import this data into a SAS dataset, selecting only specific columns and filtering based on a condition. Which code snippet correctly imports the data, performs the selection, and applies the filter?
 
 
 
 
 
Option E is the correct answer. It uses the ‘proc import’ procedure to import the CSV data into the SAS dataset ‘customer _ info’- The ‘getnames=yes’ option automatically assigns column names from the CSV file. The ‘keep’ statement selects the desired columns (‘name’, ‘city’, ‘age’, ‘state’). The ‘where’ clause filters the data to include only records where ‘age’ is greater than 25. Option A uses the ‘infile’ statement, which is suitable for delimited files but doesn’t offer the ‘where’ clause for filtering. Option B uses ‘output’ , which works but is less efficient than the ‘where’ clause. Option C incorrectly uses ‘select’ instead of ‘keep’ _ Option D uses the ‘keep’ statement but doesn’t include the ‘where’ clause for filtering.NEW QUESTION 226You need to create a report summarizing sales data by product and month, including the total sales for each product and month, as well as the cumulative total sales for each product across all months. Which code snippet would you use in the DATA step to calculate the cumulative total sales?
 
 
 
 
 
The SUM statement in SAS can only be used to calculate the sum of values within a single observation, not across observations. To calculate the cumulative total sales for each product across all months, you need to use the ‘first.Product’ condition to initialize ‘CumulativeTotal’ for each new product and then add the current ‘Sales’ value to the accumulating ‘CumulativeTotal’ variable. Option A correctly uses the ‘first. Product’ condition and adds the current ‘Sales’ value to the accumulating ‘CumulativeTotal’ variable. Option B is incorrect because the ‘sum(Sales) by Product’ expression would only calculate the total sales for each product, not the cumulative total across all months. Option C is incorrect because the ‘sum(Sales) by Month’ expression would only calculate the total sales for each month, not the cumulative total across all months. Option E is incorrect because the ‘sum(Sales) by Product Month’ expression would only calculate the total sales for each product and month, not the cumulative total across all months. Option D is also correct because the ‘first. Month’ condition would initialize ‘CumulativeTotal’ for each new month, and then the current ‘Sales’ value would be added to the accumulating ‘CumulativeTotal’ variable. This would correctly calculate the cumulative total sales for each product across all months. Both Option A and D would provide the same cumulative total sales, but they differ in how they handle the initialization of the ‘CumulativeTotal’ variable. Option A initializes ‘CumulativeTotal’ when a new product is encountered, while option D initializes it when a new month is encountered. Both approaches are valid for calculating the cumulative total sales for each product across all months.NEW QUESTION 227Which PROC PRINT statement controls the order of the variables displayed in the report?
 SELECT
 DROP
 VAR
 KEEP
In PROC PRINT, the VAR statement is used to control the order of the variables displayed in the report. You can list the variables in the order you want them to appear. The KEEP statement can control which variables appear, but not their order. DROP and SELECT are not valid statements within PROC PRINT for controlling the order of variables.References* SAS documentation for PROC PRINT.NEW QUESTION 228Which statement is true regarding the XLSX engine in the LIBNAME statement?
 The individual worksheets are automatically concatenated when reading a Microsoft Excel workbook.
 The XLSX engine can road and write data in Microsoft Excel workbooks.
 The XLSX engine can road Microsoft workbooks with both XLSX and XLS extensions
 The XLSX extension in the Microsoft Excel workbook name is optional in the LIBNAME 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 StatementNEW QUESTION 229You have a dataset ‘Orders’ with variables ‘CustomerlD’, ‘OrderDate’, ‘OrderStatus’, and ‘OrderAmount’. You need to generate a report that summarizes the average ‘OrderAmount’ for each ‘OrderStatus’ and ‘OrderDate’ combination. Additionally, you want to include the total number of orders for each order status. Which PROC MEANS statement would you use to achieve this, while also ensuring the output dataset includes the ‘OrderDate’ variable for further analysis?
 
 
 
 
 
The correct answer is option D. This statement uses the ‘FIRSTOrderDate’ option in the OUTPUT statement, which ensures that the ‘OrderDate’ variable is included in the output dataset and retains the value of ‘OrderDate’ for the first occurrence of each unique combination of ‘OrderStatus’ and ‘OrderDate’. Option A incorrectly calculates the total number of orders for each combination of ‘OrderDate’ and ‘OrderStatus’. Option B uses the correct order of variables in the CLASS statement but does not include the ‘OrderDate’ variable in the output. Option C uses an incorrect syntax for including the ‘OrderDate’ variable. Option E includes ‘OrderDate’ in the output but uses ‘LAST.OrderDate’, which would give the last occurrence of ‘OrderDate’ for each combination. Therefore, Option D is the most accurate way to achieve the desired output.NEW QUESTION 230You have two SAS data sets, ‘SALES QI’ and ‘SALES Q2’, each containing sales data for the first and second quarters respectively. Both data sets have the same variables but in different orders. How would you combine these two data sets vertically, ensuring the variables are in a specific order (e.g., ‘Region’, ‘Product’, ‘Sales’) in the combined dataset?
 data combined sales; set SALES QI SALES Q2;
 run; data combined sales; set SALES _QI SALES_Q2 (keep-Region Product Sales); run;
 QI SALES_Q2; if _ N = 1 then call symput(‘vars’,’Region Product Sales’); array vars(‘) $ vars; data combined sales; set SALES drop i; do i = 1 to dim(vars); combined_sales[vars[i]] = vars[i]; end; run;
 data combined sales; set SALES _QI SALES_Q2 (drop-Region Product Sales); run;
 data combined sales; set SALES _QI SALES_Q2; output; run;
Option C correctly combines the data sets vertically using the SET statement and then dynamically reorders the variables using an array and the call symput function to ensure the specific variable order Options A and E simply combine the data sets without specifying order, and Option B and D drop variables, which is not desired. Option C utilizes the _ N _ system variable, which is essential for dynamic variable ordering.NEW QUESTION 231Which PROC MEANS step generates the report below?
 proc means data=class mean std;var Height Weight;run;
 proc means data=class / mean std;mean (Height, Weight) ;std (Height, Weight) ;run;
 proc means data=class;var mean std;run;
 proc means data=class;options mean std;keep Height Weight;run;
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.NEW QUESTION 232You are analyzing sales data and need to present the sales amount in a report with different currency symbols based on the country of origin. Which of the following methods is the most efficient way to apply the correct currency format to the sales amounts using the FORMAT statement for temporary attributes?
 Use a separate FORMAT statement for each country specifying the currency symbol in the format definition.
 Use a single FORMAT statement with a conditional expression within the format definition to determine the appropriate currency symbol based on the country
 Create a custom format using a macro that dynamically defines the currency symbol based on the country.
 Use a PROC FORMAT step to create a permanent format that maps each country to its corresponding currency symbol, and apply this format to the sales amount variable.
 Use a SAS data step to create a new variable with the formatted sales amount using the PUT function and a format definition that includes the appropriate currency symbol.
The most efficient approach is to create a custom format using a macro that dynamically defines the currency symbol based on the country. This allows for flexibility and avoids repetitive code. While a single FORMAT statement with a conditional expression could work, it becomes less manageable as the number of countries increases- Creating a permanent format in PROC FORMAT is suitable for consistent currency conversions across multiple reports, but it requires a separate step and may not be ideal for dynamic currency changes. Using a SAS data step with the PUT function is a viable option, but it introduces an extra step in the data processing. Therefore, option C, using a custom format with a macro, offers the best balance of efficiency and flexibility.NEW QUESTION 233You are analyzing a dataset ‘CustomerSurvey’ with variables ‘AgeGroup’, ‘Satisfaction’, and ‘Gender’. You need to create a two-way table showing the distribution of ‘Satisfaction’ across different ‘AgeGroup’s. However, you only want to include customers whose ‘Gender’ is ‘Female’. Which PROC FREQ statement, with the CROSSLIST option, correctly produces this table?
 
 
 
 
 
The ‘WHERE statement in PROC FREQ allows you to filter the data based on a condition before generating the table. Therefore, ‘WHERE Gender=’Female” filters the dataset to include only observations where ‘Gender’ is ‘Female’- Options B and C are incorrect because they apply different filters. Option B filters for ‘Male’ and option C attempts to use a ‘BY ‘ statement, which is not the appropriate way to filter data in PROC FREQ. Option D is incorrect because it uses ‘IF which is not a valid statement for filtering in PROC FREQ Option E is incorrect because it filters for ‘Female’ AND ‘Satisfied’, which is not what the question requires.NEW QUESTION 234Which step reads the SASHELP. BASEBALL data set and creates the temporary data set CATCHERS?
 data sashelp. catchers;set sashelp.baseball;where position=’c’;run;
 data sashelp.baseball;set catchers;where position=’c’;run;
 data sashelp.baseball;set sashelp.catchers;where position=’c’;run;
 data catchers;set sashelp.baseball;where position=’c’;run;
The step that reads the SASHELP.BASEBALL data set and creates the temporary data set CATCHERS is:data catchers;set sashelp.baseball;where position=’C’;run;This DATA step creates a new data set named catchers by reading in data from sashelp.baseball and selecting only those observations where the variable position has the value ‘C’, which stands for catchers.NEW QUESTION 235You have a dataset named ‘SALES’ containing sales records for different products. You need to sort the dataset by ‘PRODUCT ID’ in ascending order and then by ‘SALES DATE’ in descending order. Which PROC SORT statement achieves this?
 
 
 
 
 
Option C is the correct answer. The BY statement specifies the variables to sort by, and the order is important. ‘PRODUCT_ICY is sorted first in ascending order, followed by ‘SALES_DATE’ in descending order. The DESCENDING keyword explicitly specifies the descending order for ‘SALES_DATE’. Options A and B have incorrect order of sorting, while option D sorts by ‘SALES_DATE’ first and then ‘PRODUCT ID’. option E uses nested BY statements, which is not the intended way to sort by multiple variables.NEW QUESTION 236You 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?
 
 
 
 
 
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 237You have a SAS dataset with a date variable ‘START DATE’ in the format ‘YYYY-MM-DD’. You want to calculate the number of days between the ‘START DATE’ and the current date. Which SAS code snippet achieves this correctly?
 
 
 
 
 
The correct code snippet uses the ‘intck’ function to calculate the difference between two dates in days. Option ‘C’ correctly uses the ‘day’ interval and the function to get the current date. Other options use incorrect intervals or functions, leading to incorrect results.NEW QUESTION 238You have a dataset called ‘SALES’ with variables ‘REGION’, ‘PRODUCT’, and ‘SALES AMOUNT’. You want to sort the data in ascending order by ‘REGION’ and then in descending order by ‘SALES AMOUNT’ within each region. Which PROC SORT statement achieves this?
 PROC SORT DATA=SALES; BY REGION DESCENDING; RUN,
 PROC SORT DATA=SALES OUT-SORTED SALES; BY REGION, SALES AMOUNT DESCENDING; RUN,
 PROC SORT DATA=SALES, BY REGION ASCENDING, SALES AMOUNT DESCENDING; RUN,
 PROC SORT DATA=SALES, BY SALES AMOUNT DESCENDING, REGION ASCENDING; RUN,
 PROC SORT DATA=SALES, BY REGION, SALES AMOUNT; RUN,
The correct statement is “PROC SORT DATA-SALES; BY REGION ASCENDING, SALES_AMOUNT DESCENDING; RUN,” The BY statement specifies the variables to sort by, and the order of the variables determines the sorting priority. Here, ‘REGION’ is sorted ascending first, followed by ‘SALES_AMOUNT’ sorted descending within each region.NEW QUESTION 239You are tasked with analyzing customer data, specifically identifying customers who have made more than 3 purchases in the last 6 months. You need to use the OUTPUT statement to create a new dataset containing only these high-frequency customers. Which code snippet would you use to achieve this?
 
 
 
 
 
Option D is the correct answer. This code snippet effectively identifies and outputs high-frequency customers. It first filters transactions within the past six months using the intnx function. The do loop then increments a count variable for each purchase within the timeframe. If the count exceeds 3, the output statement writes the customer’s information to the HighFrequencyCustomers dataset. The by customer_id statement ensures that the count is reset for each new customer. Option A is incorrect because it lacks the by statement, causing the count variable to accumulate across all customers. Option B is incorrect for the same reason as Option A – it doesn’t reset the count variable for different customers. Option C is incorrect as it omits the do loop, leading to a single increment in the count variable, regardless of multiple purchases within the timeframe. Option E is incorrect because the final output statement within the by group will output all transactions within a customer’s six-month period, not just those that qualify as high- frequency (count > 3).NEW QUESTION 240Which PROC PRINT option displays variable labels in the report?
 SHOWLABELS
 COLS
 LABELS=
 LABEL
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]NEW QUESTION 241You are working with a dataset containing sales transactions. You need to create a summary table that shows the total sales for each quarter of the year. Which code snippet would you use to achieve this, incorporating the OUTPUT statement to control output timing?
 
 
 
 
 
Option A is the correct answer. The code uses the intnx function to calculate the quarter for each transaction. Then, the if statement checks if the current quarter is different from the previous quarter, and if so, it outputs the current record to the SalesSummary dataset. This ensures that only one record for each quarter is output, allowing for a summary table with total sales per quarter. The total_sales variable accumulates the sales for each quarter using the ‘+’ operator. The by quarter statement groups the data by quarter and ensures that records within the same quarter are processed consecutively Option B is incorrect. The if first.quarter statement will only output the first record of each quarter, not the last. Option C is incorrect. This code will output all records in the Sales Transactions dataset, resulting in a dataset with every transaction, rather than a summary table. Option D is incorrect. The if last-quarter statement will only output the last record of each quarter, not the first. Option E is incorrect. The output statement is not used correctly The total_sales variable is calculated using the sum function, but the output statement will output all records from the SalesTransactions dataset, rather than a summary table.NEW QUESTION 242You have a dataset named ‘SALES’ with variables ‘ORDER D’, ‘PRODUCT ID’, ‘SALES DATE’, ‘QUANTITY, ‘PRICE’, ‘DISCOUNT’. You want to create a new dataset named ‘SALES SUMMARY’ that contains only the orders placed in the month of January 2023, and includes the variables ‘ORDER ID’, ‘PRODUCT ID’, and ‘TOTAL VALUE’. The ‘TOTAL VALUE’ is calculated as ‘QUANTITY ‘ PRICE ‘ (1 – DISCOUNT)’. Which of the following DATA step code snippets would achieve this?
 
 
 
 
 
Option D is the correct snippet It uses ‘KEEP=’ within the ‘SET’ statement to select only the ‘ORDER_ID’ and ‘PRODUCT_ID’ variables for output. The ‘IF’ statement filters the data to include only orders from January 2023. The ‘TOTAL _ VALUE’ variable is calculated within the DATA step- Option A is incorrect because it includes ‘SALES_DATE’ which is not needed in the final output_ Option B is incorrect because ‘KEEP:’ is used outside the ‘SET’ statement, which is not valid syntax. Option C is incorrect because it drops ‘QUANTITY’, ‘PRICE’, and ‘DISCOUNT while still using them in the calculation. Option E is incorrect because it attempts to ‘KEEP’ the ‘TOTAL _ VALUE’ variable but the variable is not yet created at that point in the code.NEW QUESTION 243Which of the following SAS code snippets will result in a syntax error? Select all that apply.
 
 
 
 
 
Option B is incorrect as SAS does not support assigning a value to an expression. Option E is incorrect as the age variable is numeric, but the assignment is a character string. All other options are valid SAS syntax. Option A creates a new variable and assigns 10 if the age is greater than 10. Option C assigns the age value plus 10 to the age variable if the age is greater than 10. Option D is a valid SAS syntax with a conditional assignment.NEW QUESTION 244You have a dataset ‘SalesData’ with variables ‘Region’, ‘Product’, ‘Sales’, and ‘Discount’. You need to create a new variable ‘SalesCategory’ that classifies sales based on the following logic: 1. If ‘Sales’ is greater than 1000 and ‘Discount’ is greater than 0.1, then assign ‘High Sales with Discount’. 2. If ‘Sales’ is greater than 1000 and ‘Discount’ is less than or equal to 0.1, then assign ‘High Sales without Discount’. 3. If ‘Sales’ is less than or equal to 1000 and ‘Discount’ is greater than 0.1, then assign ‘Low Sales with Discount’. 4. If ‘Sales’ is less than or equal to 1000 and ‘Discount’ is less than or equal to 0.1 , then assign ‘Low Sales without Discount’. Which of the following DATA step code snippets will achieve this correctly?
 
 
 
 
 
Option B is the correct code snippet because it uses nested IF-THEN/ELSE statements to accurately capture the logic. It checks for the ‘Sales’ condition first, then uses nested IF-THEN/ELSE within the ‘DO’ blocks to further categorize based on the ‘Discount’ value. The other options either miss conditions or dont handle the logic in the correct order. Loading …






	
	

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Post date: 2025-02-19 09:19:36

Post date GMT: 2025-02-19 09:19:36

Post modified date: 2025-02-19 09:19:36

Post modified date GMT: 2025-02-19 09:19:36