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SASInstitute A00-215 Exam is a computer-based exam that consists of 60 multiple-choice and short-answer questions. Candidates have 110 minutes to complete the exam, and they must achieve a score of at least 70% to pass. A00-215 exam can be taken at SAS authorized testing centers or online through Pearson VUE. Upon passing the exam, candidates will receive the SAS Certified Associate: Programming Fundamentals Using SAS 9.4 certification, which is recognized by employers and organizations around the world.

SASInstitute A00-215 (SAS Certified Associate: Programming Fundamentals Using SAS 9.4) is a valuable certification for individuals who want to pursue a career in data analysis, research, or business intelligence. A00-215 exam covers a wide range of topics and tests candidates' knowledge of SAS programming fundamentals. SASInstitute A00-215 certification is recognized globally and is highly valued by employers in different industries.

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SASInstitute A00-215 (SAS Certified Associate: Programming Fundamentals Using SAS 9.4) Certification Exam is an industry-recognized certification for individuals who are interested in pursuing a career in data science, statistics, or analytics. A00-215 Exam is designed to test an individual's knowledge and skills in programming essentials such as data handling, data manipulation, data analysis, and reporting, and is an important credential for those who wish to work with SAS software.

SASInstitute SAS Certified Associate: Programming Fundamentals Using SAS 9.4 Sample Questions (Q151-Q156):

NEW QUESTION # 151
You have a SAS dataset named 'CUSTOMERS' with variables 'NAME', 'AGE', 'CITY', and 'STATE'. You need to create a new dataset named YOUNG CUSTOMERS' containing only customers under 30 years old, residing in 'New York' state and having a name starting with 'J'. Which WHERE statement would you use in the DATA step?

A. WHERE AGE < 30 AND STATE = 'New York' AND NAME LIKE 'J%'
B. WHERE AGE < 30 AND STATE = 'NY AND NAME CONTAINS 'J';
C. WHERE AGE < 30 AND STATE = 'NY AND NAME STARTS WITH
D. WHERE AGE < 30 AND STATE = 'New York' AND SUBSTR(NAME, 1, 1) =
E. WHERE AGE < 30 AND STATE = 'New York' AND NAME LIKE 'J%'

Answer: A

Explanation:
Option A uses the correct syntax for the WHERE statement and correctly combines the conditions- Option Buses the 'STARTS WITH' function which is not a valid SAS function. Option C uses the SUBSTR function, which is not commonly used for this purpose in the WHERE statement Option D uses the 'CONTAINS' function which is not a valid SAS function- Option E is the same as option A, making it redundant The WHERE clause is applied during the data step's input phase, selectively reading only the observations that meet the specified conditions.

NEW QUESTION # 152
You have a dataset with a variable 'SALES' containing values like 123.45, 67.89, 90.12. You need to round these values to the nearest integer. Which SAS code snippet accomplishes this correctly?

Answer: C,E

Explanation:
Both options A and E achieve the desired result of rounding to the nearest integer. Option A uses the 'round' function without specifying the number of decimal places, implicitly rounding to the nearest integer. Option E explicitly uses 'round(SALES, 0)' to round to 0 decimal places, achieving the same outcome. Options B, C, and D use different functions (INT, FLOOR, CEIL) that do not provide the intended rounding behavior.

NEW QUESTION # 153
You are working with a SAS dataset containing both numeric and character variables. You want to check if there are any missing values in the dataset. Which SAS function would you use to achieve this?

A. NMISS()
B. SUM()
C. COUNT()
D. N()
E. MISSING()

Answer: A

Explanation:
The NMISS() function is specifically designed to count the number of missing values in a dataset. It considers missing values in both numeric and character variables. While other functions like COUNT() and SUM() might be used in specific scenarios, they are not directly focused on identifying missing values.

NEW QUESTION # 154
You have a dataset containing product information, including a variable named 'Category' with values such as 'Electronics', 'Clothing', 'Food', and 'Furniture'. You want to create a report that displays the total sales for each category, but you want to group 'Food' and 'Furniture' together under a new label 'Home & Living'. Which approach would you use?

A. PROC MEANS DATA=Your Data set; CLASS Category; VAR sales; LABEL Category = 'Home & Living' WHEN (Category='Food' OR Category='Furniture'); RUN;
B. PROC PRINT DATA=Your Data set; LABEL Category = 'Home & Living' WHEN (Category='Food' OR Category-'Furniture'); RUN;
C. PROC REPORT DATA-Your Data set; CLASS Category; DEFINE Sales / SUM; LABEL Category = 'Home & Living' WHEN (Category='Food' OR Category-'Furniture'); RUN;
D. PROC SUMMARY DATA-Your Data set; CLASS Category; VAR sales; OUTPUT OUT-Summary; LABEL Category = 'Home & Living' WHEN OR Category='Furniture'); RUN;
E. PROC SOL; SELECT SUM(Sales), CASE WHEN Category IN ('Food', 'Furniture') THEN 'Home & Living' ELSE Category END AS Category FROM Your Data set GROUP BY Category; RUN;

Answer: E

Explanation:
The correct answer is B. PROC SQL allows you to create a new variable that groups the desired categories using a CASE statement and the IN operator. The code 'SELECT SUM(Sales), CASE WHEN Category IN ('Food', 'Furniture') THEN 'Home & Living' ELSE Category END AS Category FROM YourDataset GROUP BY Category' groups the 'Food' and 'Furniture' categories under 'Home & Living' while maintaining the original 'Electronics' and 'Clothing' categories, calculating the total sales for each group. Option A is incorrect as the LABEL statement in PROC SUMMARY doesn't support the WHEN clause_ Option C is incorrect because PROC REPORT doesn't have the LABEL statement, and while it has WHEN clause, its used for defining attributes for report columns. Option D is incorrect as PROC PRINT is not suitable for grouping categories and calculating sums. Option E is incorrect as the LABEL statement in PROC MEANS only allows for assigning labels to existing values, not creating new groups.

NEW QUESTION # 155
You have a dataset 'CustomerData' with variables 'CustomerlD', 'Age', and 'Income'. You want to create a new dataset 'CustomerSegments' with 'CustomerlD' and 'Segment' variables. The 'Segment' variable should be assigned based on the following rules: 1. If 'Age' is less than 30 and 'Income' is greater than 50,000, assign 'Young Professionals'. 2. If 'Age' is between 30 and 50 and 'Income' is greater than 75,000, assign 'Mid-Career Earners'. 3. If 'Age' is greater than 50 and 'Income' is greater than 100,000, assign 'Senior Executives'. 4. For all other cases, assign 'Other'. Which of the following code snippets will produce the correct 'CustomerSegments' dataset?

Answer: C

Explanation:
Option B is the correct code snippet as it accurately applies the conditional logic. It uses nested IF- THEN/ELSE statements to capture the multiple conditions based on 'Age' and 'Income', and the 'Segment' is assigned appropriately based on the rules. Options A, C, and D have incorrect conditions or miss some of the specified logic. Option E correctly uses 'output' but is not needed in this case as the default behavior in the DATA step is to output the data.

NEW QUESTION # 156
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