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SASInstitute SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Sample Questions (Q57-Q62):

NEW QUESTION # 57
You are working with a dataset containing sales data, where the 'SalesRegion' variable can have values like 'North', 'South', 'East', 'West', or be missing. You want to create a custom format that labels missing values as 'Unknown' and uses different labels for specific regions: 'North' becomes 'Northern Region', 'South' becomes 'Southern Region', and so on. Which PROC FORMAT code snippet correctly defines this format?

Answer: C

Explanation:
The correct answer is Option A. It accurately defines the custom format 'REGION FORMAT by explicitly mapping each specified region value to a more descriptive label and using 'OTHER' to handle any other value, including missing values. Options B and C use 'MISSING' or '.' directly, which are not the correct way to handle any non-specified values, including missing values. Options D and E are incorrect because they unnecessarily use both 'OTHER' and or 'MISSING', which can cause unexpected behavior.

NEW QUESTION # 58
Given the data set NAMES:

Which PROC SORT program creates the NAMES data set shown below?

A. proc sort data=Names;
orderby Age;
run;
B. proc sort data=Names;
by Age;
run;
C. proc sort data=Names;
by Name;
run;
D. proc sort data=Names;
orderby Name;
run;

Answer: C

Explanation:
The PROC SORT step in SAS is used to sort data sets by one or more variables. The syntax for the proc sort step requires the by statement to specify the variable(s) by which to sort the data. The correct answer is option A:
proc sort data=Names;
by Name;
run;
This sorts the data set by the variable 'Name' in alphabetical order, which matches the sorted NAMES data set shown in the question. Option B uses orderby, which is not a valid SAS statement. Option C sorts by 'Age', which would not give the same result as shown in the question. Option D also incorrectly uses orderby.

NEW QUESTION # 59
You are working with a large dataset containing financial transactions. You need to analyze the average transaction amount for each customer, but only for transactions that occurred during the last quarter of the year. You also want to count the total number of transactions for each customer during that period. Which of the following DATA step code snippets will correctly achieve this analysis, considering efficiency and best practices? Select all that apply.

Answer: A,C

Explanation:
The most efficient and effective ways to achieve the analysis are options A and B. Both options correctly filter transactions to the last quarter, group by customer ID, and calculate the average amount and transaction count using 'by' processing and conditional logic. Option A uses a 'first-customer_id' and 'last-customer_id' approach to handle the initialization and final output. This is a common approach in SAS data step programming. Option B also uses 'first-customer_id' and 'last-customer_id' but employs the 'retain' statement to maintain the values of 'average_amount' and 'transaction_count' throughout the processing of each customer's transactions. This approach is slightly more concise and avoids unnecessary re-initialization within each customer's group. Option C uses arrays, which could be inefficient in this scenario due to the potential large size of the dataset and the need to manage a separate array element for each unique customer ID. It also requires a fixed Size array, which might not be suitable for a dataset with many distinct customer IDs. Option D incorrectly uses the 'sum' function, which would not produce the desired average amount. Option E incorrectly uses the 'mean' function, which is not appropriate for calculating an average within a group. This approach would result in incorrect results, as it attempts to calculate the mean across the entire dataset instead of within each customer group. The correct approach is to use the 'sum' function to accumulate the total amount and then divide by the number of transactions in each group.

NEW QUESTION # 60
You 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?

Answer: C,E

Explanation:
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 # 61
Which PROC MEANS statements specifies variables to group the data before calculating statistics?

A. VAR
B. GROUP
C. SUMBY
D. CLASS

Answer: D

Explanation:
In the context of the PROC MEANS procedure in SAS, the CLASS statement is used to specify categorical variables whose unique values group the data before calculating statistics. This allows PROC MEANS to calculate statistics like the mean, sum, or standard deviation for each class or group of data defined by the CLASS variables.
The CLASS statement works as follows:
* class variable-list; specifies one or more variables to define groups for analysis.
* When used, PROC MEANS computes the requested statistics for each level of the variables listed, which can be very helpful for analyzing how groups compare across different statistics.
The other options are not used in the PROC MEANS procedure to specify grouping of data:
* B. GROUP is not a valid statement in PROC MEANS.
* C. SUMBY is not a valid statement in SAS.
* D. VAR is used in PROC MEANS to specify the analysis variables for which statistics are to be computed, not to group the data.

NEW QUESTION # 62
......

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A00-215 TEST BRAINDUMPS - A00-215 PASS-SURE TORRENT & A00-215 TEST QUESTIONS

A00-215 Test Braindumps - A00-215 Pass-Sure Torrent & A00-215 Test Questions