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The SAS Certified Associate: Programming Fundamentals Using SAS 9.4 certification is a valuable asset for individuals who want to pursue a career in the field of data management, analytics or business intelligence. SAS Certified Associate: Programming Fundamentals Using SAS 9.4 certification demonstrates the individual’s expertise in SAS programming language and their ability to work with data using SAS tools. To prepare for the exam, individuals can take advantage of SAS Institute’s training courses, online resources and study materials.

SASInstitute SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Sample Questions (Q282-Q287):

NEW QUESTION # 282
Consider a dataset 'SURVEY' with a variable 'SATISFACTION' that captures customer satisfaction levels. You are tasked with generating a frequency report for 'SATISFACTION' but need to suppress both cumulative frequencies and percentages. Additionally, you want to calculate the percentage of customers in each satisfaction level category relative to the total number of responses. Which PROC FREQ statement achieves this requirement?

Answer: A

Explanation:
The correct answer is A. The 'NOCIJM' and 'NOPERCENT options in the 'TABLES statement suppress the display of cumulative frequencies and percentages, respectively The 'OUTPCT option calculates the percentage of observations in each category relative to the total number of observations, which is what you need to show the percentage of customers in each satisfaction level category The other options would not provide the desired output.

NEW QUESTION # 283
Which PROC PRINT step correctly displays only the first 10 observations in the data set?

A. proc print data=sashelp.class(obs=l10);
run;
B. proc print data=sashelp.class (obaS'10' )
; run;
C. proc print data=sashelp.class obs=10;
run;
D. proc print data=sashelp.class;
obs=10;
run;

Answer: A

Explanation:
Option A is correct. The (obs=10) option is used as a data set option within the proc print statement to limit the number of observations processed to the first 10. The syntax should be placed immediately after the data set name within parentheses. Options B, C, and D are incorrect because they either use the wrong option or place the obs option incorrectly outside of the parentheses or with incorrect syntax.
References:
* SAS 9.4 documentation on the obs= data set option.

NEW QUESTION # 284
You have a dataset with a variable 'Age' containing missing values. You need to create a format that labels all missing values as 'Unknown Age' and all other values as 'Age Provided'. Which PROC FORMAT code snippet achieves this?

Answer: A

Explanation:
The correct answer is A- The 'OTHER keyword in PROC FORMAT allows you to define a label for any value that doesn't explicitly match a previous range or value in the format. In this case, 'LOW - HIGH = 'Age Provided" covers all non-missing values, while 'OTHER covers any missing values. Option B uses a period (e), which represents a missing value, but this approach is less flexible for different missing value types. Option C uses the 'MISSING' keyword, which is specific to missing values and might not handle all possible missing value representations. Options D and E are incorrect because they explicitly label only a specific string (blank space or 'Unknown Age').

NEW QUESTION # 285
Which of the following SAS variable names are valid and adhere to the SAS naming conventions? 1 . _ age 2. Age_ 1 3. AGE 1 4. lage 5. Age! 6. Age_ 7. _1Age 8. _age_ 9. Age_l_

A. 1, 3, 6 and 9
B. 2, 3, 6 and 9
C. 1, 2, 3 and 9
D. 2, 3, 6 and 7
E. 1, 2, 3 and 6

Answer: B

Explanation:
SAS variable names must start with a letter or an underscore (_), and can contain letters, numbers, and underscores. They cannot contain special characters like The variable name can be up to 32 characters long. 1. '_age' is a valid SAS variable name. 2. 'Age_l' is a valid SAS variable name. 3. 'AGE _ 1' is a valid SAS variable name. 4. 'lage' is not a valid SAS variable name because it starts with a number. 5. 'Age" is not a valid SAS variable name because it contains a special character. 6. 'Age_' is a valid SAS variable name. 7. '_1Age' is not a valid SAS variable name because it starts with a number. 8. '_age_' is a valid SAS variable name. 9. 'Age_l_' is a valid SAS variable name.

NEW QUESTION # 286
You have a dataset 'CustomerData' containing 'CustomerlD', 'Age', 'Income', 'PurchaseAmount', and 'PurchaseDate'. You want to analyze the average purchase amount for customers in different age groups. You need to group customers based on their age into three categories: 'Young' (18-30), 'Middle-Aged' (31-50), and 'Senior' (51+). Which code snippet would you use to correctly group customers and calculate the average purchase amount for each age group using PROC MEANS?

Answer: B

Explanation:
This approach first creates a new dataset 'AgeGroups' where customers are categorized based on their age using IF-THEN statements. Then, PROC MEANS is applied to this new dataset with 'AgeGroup' in the CLASS statement to calculate the average purchase amount for each age group. Option A uses 'Age' directly as a CLASS variable, which would not create the desired age groups. Option B does not categorize customers into age groups. Option D uses WHERE statements to filter data but does not categorize customers into age groups. Option E uses incorrect syntax for specifying ranges in the WHERE statement.

NEW QUESTION # 287
......

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HIGH-QUALITY VALID TEST A00-215 BOOTCAMP - 100% PASS-RATE SOURCE OF A00-215 EXAM

High-quality Valid Test A00-215 Bootcamp - 100% Pass-Rate Source of A00-215 Exam