SASInstitute - A00-215 - Latest Exam SAS Certified Associate: Programming Fundamentals Using SAS 9.4 PDF

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SASInstitute SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Sample Questions (Q267-Q272):

NEW QUESTION # 267
You're working with a dataset 'CUSTOMER DATA' containing customer information, including 'CUSTOMER ID', 'AGE', 'INCOME', and 'CITY'. You need to analyze customer spending patterns based on income level, but only for customers residing in 'New York City'. Which code snippet correctly subsets the data for this analysis?

Answer: E

Explanation:
The correct code snippet is option E. It correctly sub-sets the 'CUSTOMER_DATA dataset by using the 'if statement to filter observations where 'CITY' is 'New York City'. The 'output' statement then directs the qualifying observations to the new dataset 'NYC_CUSTOMERS'. Option A only filters for customers in 'New York City' but doesn't create a new dataset. Option B uses a semi-colon ';' after the 'if statement, which would result in an error. Option C attempts to output to 'NYC CUSTOMERS' inside the 'then' clause, which is incorrect syntax. Option D uses a semi-colon after the 'if statement and doesn't use 'then' for output, resulting in syntax errors.

NEW QUESTION # 268
You have a SAS dataset 'PRODUCT SALES' with variables 'PRODUCT ID', 'SALES DATE', 'QUANTITY SOLD', and PRICE'. You need to create a new dataset 'MONTHLY SALES' that only includes observations where 'SALES DATE' falls within the month of June (regardless of year) and the 'PRICE' is greater than 100. Which code snippet would you use to achieve this?

Answer: E

Explanation:
The correct code snippet is option B. It utilizes the 'month' function to extract the month from the 'SALES_DATE' variable and compares it to 6 (June). Additionally, it incorporates the 'PRICE' condition, ensuring only observations with 'PRICE' greater than 100 are included in the new dataset 'MONTHLY_SALES'. Option A only filters by month, neglecting the price criterion. Option C filters for a price less than 100, which contradicts the requirement. Option D attempts to compare the 'SALES DATE' variable to a string 'June', which is incorrect syntax. Option E only filters by month and lacks the 'then' statement for outputting to the new dataset.

NEW QUESTION # 269
You have a dataset with customer purchase records, including their customer ID, purchase date, and purchase amount. You want to calculate the average purchase amount for each customer but only for the purchases made in the last three months. Using the DATA step, how would you modify the code to achieve this, taking advantage of BY group processing with FIRST. and LAST. variables?

A. data customer_avg; set purchases; by customer_id; retain avg_amount 0; count=0; if first.customer_id then count+l; if then avg_amount+purchase_amount; output; run; data customer_avg; set purchases; by customer_id; retain avg_amount 0 count 0; if first.customer_id then do; avg_amount=0;
B. count-0; end; if intck('month',purchase_date,today())<=3 then do; avg_amount=purchase_amount; count+l; end; output; run;
C. count-0; end; if then do; avg_amount+purchase_amount; count+l; end; output; run; data customer_avg; set purchases; by customer_id; retain avg_amount 0 count 0; if first.customer_id then do; avg_amount=0;
D. =3 then do; avg_amount+purchase_amount; count+l; end; output; run; data customer_avg; set purchases; by customer_id; retain avg_amount 0; if first.customer_id then if intck('month' , purchase_date, today())< =3 then avg_amount+purchase_amount; output; run;
E. data customer_avg; set purchases; by customer_id; retain avg_amount 0 count 0; if first.customer_id then if intck('month' , purchase_date,

Answer: E

Explanation:
Option D is the correct answer because it correctly initializes the 'avg_amount' variable to zero for each customer's first record, it only considers purchases within the last three months, and it calculates the average by dividing the accumulated purchase amount by the count of transactions within the three-month window. Options A, B, C, and E either incorrectly initialize the variables or do not correctly compute the average within the specific time frame.

NEW QUESTION # 270
You are generating a report with PROC REPORT and have defined a complex set of break levels. You want to add a footnote at the end of each break level (e.g., at the bottom of each group or subtotal) that provides additional context or explanations for the data within that break. Which of the following approaches will achieve this placement of footnotes effectively?

A. Utilize the 'DEFINE statement with the T-OOTNOTE option to create a separate column for the footnote, then use the SAT LEVEL' option within the FOOTNOTE statement.
B. Combine the FOOTNOTE statement with the SAT LEVEL' option and the 'BREAK statement to ensure accurate positioning at each break level.
C. Employ the FOOTNOTE statement with the SAT PAGE option, and use the 'BREAK statement to control where the footnote should appear within the page.
D. Use the FOOTNOTE statement with the 'AT LEVEL' option, specifying the break level where the footnote should appear.
E. Include the FOOTNOTE statement within the 'BREAK statement for each level, ensuring the footnote is positioned at the end of each break.

Answer: D

Explanation:
The correct option is A: Use the FOOTNOTE statement with the 'AT LEVEL' option, specifying the break level where the footnote should appear. This approach allows for precise placement of footnotes at specific break levels within the report. Option B is incorrect because the FOOTNOTE statement cannot be placed within the 'BREAK statement. Options C and D are not supported syntax for the FOOTNOTE statement. Option E is a combination of A and B and is also incorrect.

NEW QUESTION # 271
You are analyzing data on customer purchases, and you need to generate a report showing the frequency distribution of purchase amounts categorized by customer age group (18-25, 26-35, 36-45, 46-55, 56+). You also want to examine the relationship between purchase amount and customer gender (Male, Female). Which of the following PROC FREQ statements would correctly accomplish this task and generate the desired output?

Answer: A

Explanation:
Option E is the correct answer. It generates separate frequency distributions for Purchase_Amount by Gender and Age by itself. The FORMAT statement defines a custom AGEGROUP format for the age variable to group ages into the desired categories. Option A attempts to create a three-way table, which may not be the most effective for the analysis. Option B creates two separate tables, one for age and another for purchase amount and gender, which does not effectively address the goal. Option C creates two separate tables, one for purchase amount and another for age and gender. Option D also creates two separate tables, one for purchase amount and gender, and another for age, which does not address the goal. The key to the solution is creating separate tables for the desired analysis while using the correct formatting for the age variable to achieve the desired categorization.

NEW QUESTION # 272
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SASINSTITUTE - A00-215 - LATEST EXAM SAS CERTIFIED ASSOCIATE: PROGRAMMING FUNDAMENTALS USING SAS 9.4 PDF

SASInstitute - A00-215 - Latest Exam SAS Certified Associate: Programming Fundamentals Using SAS 9.4 PDF