SAS A00-231 Exam Dumps

Given the following SAS data set WORK.CLASS:

Name Gender Age

Anna F 23

Ben M 25

Bob M 21

Brian M 27

Edward M 26

Emma F 32

Joe M 34

Sam F 32

Tom M 24

The following program is submitted: data WORK.MALES WORK.FEMALES(drop=age); set WORK.CLASS; drop gender; if Gender="M" then output WORK.MALES; else if Gender="F" then output WORK.FEMALES; run; How many variables are in the data set WORK.MALES?

Select one:

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SIMULATION

Scenario:

This project will use data set cert.input13. At any time, you may

save your program as program13 in cert\programs.

This data set contains 1001 observations and 2 variables:

o Date1, a numeric variable representing an unformatted

SAS date value. Example: 12001.

o Charnum, a character variable representing a monetary

amount. Example: $50,000.

Write a SAS program that will:

o Save the new data set as results.output13.

o Create a new variable Chdate that converts

the datel variable to a character variable that is in the

format ddmonyyyy, such as 11NOV1992.

o Create a new variable num1 that converts

the Charnum variable to a numeric variable.

What is the average (mean) of the num1 variable for the entire data set?

Enter your numeric answer in the space below (Round your answer to the nearest

integer).

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The following SAS program is submitted:

Which output is correct?

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A PROC PRINT report was created with the following title:

Asia Sports Vehicle Summary After the PROC PRINT report is run, a programmer would next like to produce a PROC FREQ report with the following title: Asia Sports Vehicle Summary Distribution by Make Which statement(s) would produce the new report titles?

Select one:

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SIMULATION

Scenario:

This project will use data set cert.input08a and cert.input08b. At

any time, you may save your program

as program08 in cert\programs.

Both data sets contain a common numeric variable named ID.

Write a program that will use a SAS DATA Step to:

o Combine data sets cert.input08a and cert.input08b by

matching values of the ID variable.

o Write only observations that are in both data sets to a

new data set named results.match08.

o Write all other non-matching observations from either

data set to a new data set named results.nomatch08.

o Exclude all variables that begin with

"ex" from results.nomatch08.

How many variables (columns) are in results.match08

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