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SASInstitute SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Sample Questions (Q55-Q60):

NEW QUESTION # 55
A SAS programmer is analyzing a dataset containing customer demographics. They want to create a new variable called 'AgeGroup' that categorizes customers into three groups based on their age: 'Young' (18-30), 'Middle-Aged' (31-50), and 'Senior' (51 +). Which of the following statements correctly illustrates the use of the LENGTH statement and the IF-THEN/ELSE logic within the DATA step to achieve this?

Answer: D

Explanation:
Option A is the only correct answer. It correctly uses the LENGTH statement to define the length of the 'AgeGroup' variable as 10 characters. The IF-THEN/ELSE logic is also correctly implemented to categorize customers into the three age groups. Option B uses the LENGTH statement without specifying a length, which will default to 8 characters. Options C, D, and E have incorrect logic for the age ranges.

NEW QUESTION # 56
You have a dataset 'ORDERS' with variables 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE', and 'TOTAL AMOUNT'. You need to sort the data by 'ORDER DATE' in ascending order and then by 'TOTAL AMOUNT' in descending order within each 'CUSTOMER ID'. Which PROC SORT statement achieves this?

Answer: A

Explanation:
The BY statement in PROC SORT allows for multiple variables to be used in a hierarchical manner, enabling sorting within groups. Option B correctly specifies the sorting order: first by 'CUSTOMER_ID', then by 'ORDER_DATE ascending within each 'CUSTOMER_ID', and finally by 'TOTAL_AMOUNT' descending within each 'CUSTOMER_ID'. Option A sorts by 'ORDER_DATE' and 'TOTAL_AMOUNT without considering 'CUSTOMER_ID', while options C, D, and E have incorrect orderings or lack the necessary grouping. Option B is the only option that achieves the desired sorting.

NEW QUESTION # 57
You have three datasets: 'CUSTOMERS', 'ORDERS', and 'PRODUCTS'. 'CUSTOMERS has 'CUSTOMER ID' and 'CITY', 'ORDERS' has 'ORDER ID', 'CUSTOMER ID', and 'PRODUCT ID', and 'PRODUCTS' has 'PRODUCT ID' and 'PRICE'. You want to create a dataset 'SALES SUMMARY' that summarizes the total sales for each product in each city. How would you use multiple MERGE statements and the IN= option to achieve this?

Answer: B

Explanation:
The correct answer is ''B''. This code uses a series of MERGE statements to join the datasets based on the shared variables. First, it merges 'CUSTOMERS' and 'ORDERS on 'CUSTOMER ID then merges the result with PRODUCTS' and 'ORDERS' (using 'ord2' to differentiate from the previous variable) on 'PRODUCT_ID' This allows the correct matching of 'PRICE and 'QUANTITY_SOLD for each order. Finally, it calculates the 'SUM_SALES by grouping by 'CITY and ' PRODUCT ID. ''Explanation of other options. A- This option only uses 'ORDERS once, so it wouldn't match the right 'QUANTITY SOLD' values. ' ''C:'' The order of merging datasets is incorrect It should merge 'CUSTOMERS' with 'ORDERS' first to connect the 'CUSTOMER_ID' and 'CITY' ' This option calculates the sum of 'PRICE only, not 'PRICE ' QUANTITY SOLD , which is necessary for the total sales. ' ''E:'' This option only groups by 'CITY , not 'PRODUCT ICY, leading to incorrect summary.

NEW QUESTION # 58
You have a SAS dataset named 'sales' with variables 'product', 'region', and 'units_sold'. You want to create a new dataset 'sales _ summary' that summarizes the total units sold for each product across all regions. Which of the following code snippets correctly achieves this?

Answer: A,C,D

Explanation:
All three code snippets (A, B, and C) correctly achieve the desired result of summarizing the total units sold for each product. Option A uses the 'PROC SQL' procedure, which is very flexible for data manipulation and aggregation. It uses the function to calculate the total units sold per product and the 'GROUP BY' clause to group the results by product. Option B uses the 'PROC SUMMARY procedure, which is designed specifically for summarizing data. The 'CLASS' statement specifies the variable to group by, the 'VAR' statement specifies the variable to summarize, and the 'OUTPUT statement defines the output dataset and the calculation to be performed (sum in this case). Option C uses the 'PROC MEANS procedure, similar to 'PROC SUMMARY but focused on generating descriptive statistics. It achieves the desired outcome by specifying the 'CLASS' and 'VAR statements and using the 'OUTPUT' statement to create the output dataset with the sum of units sold per product. Option D uses ' PROC TABULATE which is primarily for creating tables and does not directly produce a dataset with the desired summary Option E uses 'PROC TRANSPOSE to transpose the dataset which is not appropriate for this scenario.

NEW QUESTION # 59
Given the input data set INVENTORY as shown below:

Two output data sets are desired, CHIPS and OTHERSNACKS.
*The CHIPS data set should only include QtySold, Price, and Product.
*The OTHERSNACKS data set should include QtySold, Price, product, and Type.
Which Data step creates the two desired output data sets

A. data chips othersnacks;
set: inventory;
if Type="chips" then do;
keep QtySold Price Product;
output chips;
end;
else output othersnacks;
run;
B. data chips othersnack"
set inventory (keep=QtySold Price Product);
if Typo~"chips" then output chips;
else output othersnacks;
run;
C. data chips otharsnacks;
set inventory;
if Type="chips" then output chips
else output otharsnacks;
keep QtySold Price Product;
run;
D. data chips ( keep=QtySold Price Product) othersnacks;
set inventory;
if Type="chipa" then output chips;
else output otharsnacks;
run;

Answer: B

NEW QUESTION # 60
......

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Keith Fox Keith Fox

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