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A00-282
  • Exam Code: A00-282
  • Exam Name: Clinical Trials Programming Using SAS 9.4
  • Updated: Aug 21, 2026
  • No. of Questions: 144 Questions and Answers
  • Download Limit: Unlimited
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SASInstitute A00-282 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Transform or Summarize Clinical Trials Data15%- Data step manipulation
- Aggregation and transposition
- Derive variables and study days
Topic 2: Macro Programming for Clinical Trials15%- Debugging macros
- Macro variables and parameters
- Create and call macros
Topic 3: Clinical Trials Data Structures10%- CDISC standards: SDTM, ADaM
- Define.xml and dataset specifications
- Clinical domain structures
Topic 4: Validate Clinical Trial Data Reporting20%- Use PROC COMPARE and log review
- Programming validation techniques
- Validation principles
Topic 5: Report Clinical Trials Results10%- PROC REPORT and PROC PRINT
- Produce tables, listings, and figures
Topic 6: Regulatory Submissions5%- Compliance for clinical data reporting
- ICH and FDA guidelines
Topic 7: Clinical Trials Process5%- Clinical trial lifecycle and protocol
- Derive programming requirements from protocol and CRF
Topic 8: Apply Statistical Procedures for Clinical Trials15%- Basic statistical analysis for clinical data
- PROC MEANS, FREQ, TABULATE
Topic 9: Manage Clinical Trials Data5%- Use SQL and dictionary tables
- Access and explore clinical datasets
- Clean, merge, and subset data

SASInstitute Clinical Trials Programming Using SAS 9.4 Sample Questions:

1. You are working with the VS data set that contains the variable Pulse.

Which PROC SGPLOT step will produce the graph shown below?

A) proc sgplot data = Work.VS noautolegend;
density pulse;
vbarbasic pulse;
run;
B) proc sgplot data = Work.VS noautolegend;
density pulse;
histogram pulse;
run;
C) proc sgplot data = Work.VS noautolegend;
vbarbasic pulse;
density pulse;
run;
D) proc sgplot data = Work.VS noautolegend;
histogram pulse;
density pulse;
run;


2. Which name is a valid SAS V5 variable name?

A) AE_START_DTC
B) AE-STDTC
C) _AESTDTC
D) AESTARTDTC


3. Given the data set below, which SAS program correctly counts the of number of unique subjects for each combination of system organ class and treatment?

A) proc sort data=a out=b nodupkey;
by trta aesoc;
run;
proc freq data=b noprint;
by trta;
table aesoc / out=freq;
run;
B) proc sort data=a out=b;
by trta aesoc;
run;
proc freq data=b noprint;
by trta;
table aesoc / out=freq;
run;
C) proc sort data=a out=b;
by trta aesoc subjidn;
run;
proc freq data=b noprint;
by trta;
table aesoc/out=freq;
run;
D) proc sort data=a out=b nodupkey;
by trta aesoc subjidn;
run;
proc freq data=b noprint;
by trta;
table aesoc / out=freq;
run;


4. You have been asked to import an Excel spreadsheet.
What will lead to substantial differences between the original Excel spreadsheet and the resulting SAS data set?

A) the number of columns to be read from the Excel file
B) the number of rows to be read from the Excel file
C) multiple value types within a single column
D) multiple value types within a single row


5. You are working on validating the ADSL data set which contains the variables subject, sex, age, birth date, baseline height, and baseline weight. These variables are brought in from SDTM, and you derive new variables baseline BMI (from baseline height and weight) and age categorization (from age).

You get the following in the SAS log:

To reconcile this difference, which variable values will be most helpful to provide to the production programmer?

A) SUBJID BLBMI
B) AGECAT1 BLBMI
C) SUBJID BLHT BLWT BLBMI
D) BLBMI


Solutions:

Question # 1
Answer: A
Question # 2
Answer: C
Question # 3
Answer: D
Question # 4
Answer: C
Question # 5
Answer: C

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