SAS Interview Questions and Answers

This section covers frequently asked SAS interview questions.

1. What is SAS?

SAS (Statistical Analysis System) is a comprehensive software suite for advanced analytics, data management, and business intelligence. It offers both a graphical user interface and a powerful programming language.

2. Features of SAS.

  • Analytics: Provides a wide range of analytical capabilities.
  • Data Access & Management: Can connect to various data sources and manage data efficiently.
  • Business Solutions: Offers tools for business analysis and decision-making.
  • Reporting & Graphics: Produces various reports and visualizations.
  • Data Visualization: Creates charts and graphs to represent data insights.

3. Why Choose SAS Over Other Tools?

  • Ease of Learning: Relatively straightforward to learn, especially for those familiar with SQL.
  • Strong Graphics Capabilities: Makes creating visualizations relatively easy.
  • Robust Data Handling: Excellent for managing and processing large datasets.
  • Well-Tested Updates: Updates are rigorously tested, leading to more reliable software.
  • High Job Demand: Strong job market for SAS professionals.

4. Main Capabilities of the SAS Framework.

  1. Data Access: Connecting to various data sources.
  2. Data Management: Cleaning, transforming, and preparing data for analysis.
  3. Data Analysis: Performing statistical and other analyses.
  4. Data Presentation: Creating reports and visualizations.

5. Data Types in SAS.

SAS primarily uses numeric and character data types. Date values are stored as character data but have special functions for date manipulation.

6. Main Functions of SAS.

  • Business planning
  • Data warehousing
  • Statistical analysis
  • Data management
  • Quality management
  • Information retrieval
  • Operational research and decision support

7. Essential Components of SAS Programming.

  • Variables: Represent data items.
  • Datasets: Organized collections of data.
  • Statements: Instructions within a SAS program.

8. Basic SAS Syntax Rules.

  • Statements end with semicolons (;).
  • Multiple statements can be on one line, separated by semicolons.
  • SAS is not case-sensitive.
  • Comments are denoted by /* ... */ or * ... ;

9. What is PDV (Program Data Vector)?

The PDV is a temporary in-memory area where SAS stores and processes data during the execution of a DATA step. It's a crucial component for data manipulation.

10. SAS Datasets.

SAS datasets are tables storing data for analysis. They are organized into rows (observations) and columns (variables).

11. Use of the OUTPUT Statement.

The OUTPUT statement writes data from the PDV to a SAS dataset. It's used to save results from data steps for later use or reporting.

12. Use of the STOP Statement.

The STOP statement immediately terminates the execution of a SAS DATA step.

13. Reading Data from Datasets vs. External Files.

When reading from an existing SAS dataset, SAS retains variable values across observations. When reading from an external file, you need to define the variables and their formats in the INPUT statement.

14. When SAS Doesn't Automatically Convert Character to Numeric.

If a character variable contains non-numeric characters (like a dollar sign), SAS won't automatically convert it to a numeric value. Use explicit functions like INPUT to handle such conversions.

15. SAS BI vs. SAP BO.

Feature SAS BI SAP BO
Strength Data integration Visualization and ad-hoc analysis

16. BY-Group Processing.

BY-group processing allows you to process data grouped by one or more variables. The data must be sorted by the BY variables.

17. INPUT vs. INFILE in SAS.

INFILE specifies the external data file. INPUT defines how the data from that file is read into SAS variables.

18. DROP Option in SET and DATA Statements.

The DROP option is used in both SET and DATA statements, but it functions differently depending on the context of the statement. Here's a comparison to help understand how it behaves in each case:

1. DROP in SET Statement

The SET statement is used to assign a value to a variable or field in a database. When the DROP option is used in a SET statement, it is typically used to clear the value of a field or reset a variable. This will remove any previously assigned value, leaving it in its default state.

Example Description
SET fieldname = DROP; This statement clears the value of the field or variable, effectively setting it to an empty or default state.

2. DROP in DATA Statement

The DATA statement is used to define new variables or fields. When the DROP option is used with a DATA statement, it is typically employed to remove or drop previously defined variables from the current context, especially in a program or script. The use of DROP in this context helps clean up memory by removing variables that are no longer needed.

Example Description
DATA fieldname DROP; This removes or drops the variable or field, freeing up memory or ensuring that it is no longer available for use.

Key Differences

  • SET with DROP: Clears the value of an existing variable or field, making it effectively empty or null.
  • DATA with DROP: Removes a variable or field completely from the environment, freeing up associated resources.

19. The DATA Step in SAS.

A DATA step in SAS is a fundamental building block. It reads data, processes it, and creates or modifies SAS datasets. Each data step includes a data dictionary containing information about variables in the dataset.

20. SAS Informats.

Informats instruct SAS on how to read data from external files into SAS variables. They specify the data type and format of the input data.

Types of Informats:

  • Numeric Informats: For numeric data (e.g., INFORMAT w.d).
  • Character Informats: For character data (e.g., $INFORMAT w.).
  • Date/Time Informats: For date and time values (e.g., INFORMAT w.).

21. SAS Format vs. SAS Informat.

Feature Format Informat
Purpose How to display values How to read values
Usage Writing data Reading data

22. Sorting in SAS.

Use PROC SORT to sort a SAS dataset. The BY statement specifies the variables to sort by. The `ASCENDING` or `DESCENDING` options control the sort order.

Syntax

proc sort data=original out=sorted;
  by ascending variable1 descending variable2;
run;

23. NODUP vs. NODUPKEY Options in PROC SORT.

Option Comparison Effect
NODUP All variables Removes duplicate observations
NODUPKEY BY variables only Removes duplicate observations based on BY variables

24. PROC MEANS vs. PROC SUMMARY.

PROC MEANS calculates descriptive statistics. PROC SUMMARY is similar but more efficient for producing subgroup statistics. It requires an OUTPUT statement to save results.

25. PROC PRINT and PROC CONTENTS.

  • PROC PRINT displays the data in a SAS dataset.
  • PROC CONTENTS shows information about a dataset (variables, attributes, etc.).

26. DATA _NULL_.

DATA _NULL_ is a DATA step that doesn't create a dataset. It's used for tasks like creating macro variables or performing calculations without generating output datasets.

27. Converting Character to Numeric and Vice Versa.

  • INPUT(): Converts character to numeric.
  • PUT(): Converts numeric to character.
Example

numeric_var = input(character_var, best.);
character_var = put(numeric_var, best.);

28. _CHARACTER_ and _NUMERIC_.

_CHARACTER_ and _NUMERIC_ are automatic variables representing all character and numeric variables, respectively, in a data step. They are useful in procedures like PROC MEANS to easily specify all variables of a given type.

29. Including/Excluding Variables in SAS Datasets.

  • KEEP: Specifies variables to retain.
  • DROP: Specifies variables to exclude.

30. Character Functions for Data Cleaning.

  • TRIM(): Removes trailing blanks.
  • COMPRESS(): Removes specified characters.
  • UPCASE(): Converts to uppercase.
  • LOWCASE(): Converts to lowercase.
  • COMPBL(): Compresses multiple blanks into single blanks.

31. Saving SAS Logs to an External File.

Use PROC PRINTTO. For example: proc printto log="C:\mylog.txt" new; run;

32. The SUBSTR() Function.

SUBSTR() extracts a substring from a character string.

Syntax

substring = substr(string, start, length);

33. Creating Macro Variables in SAS.

  • %LET
  • %DO loops
  • CALL SYMPUTX
  • INTO in PROC SQL

34. Debugging Macros in SAS.

Use options like MLOGIC, SYMBOLGEN, and MPRINT in your SAS code to generate debugging information in the SAS log.

35. SYMGET vs. SYMPUT.

SYMGET retrieves the value of a macro variable. SYMPUT assigns a value to a macro variable.

36. How PROC SQL Works.

  1. Syntax check.
  2. Query optimization.
  3. Data loading.
  4. Execution.
  5. Result creation and output.

37. Counting Intervals Between Dates in SAS.

Use the INTCK function.

Syntax

interval_count = intck('month', start_date, end_date);

38. Deleting Duplicate Observations in SAS.

Several methods exist for removing duplicate observations (rows) from a SAS dataset:

  1. Using PROC SQL: This approach uses SQL's `DISTINCT` keyword to select only unique rows.
  2. Data Step Method: This method uses a data step with a `BY` group processing to identify and retain only the first occurrence of each unique observation.
  3. NODUP Option in PROC SORT: This is a simpler method, but it removes all but one of the duplicate observations.
PROC SQL Example

proc sql;
  create table unique_data as
    select distinct * from original_data;
quit;
Data Step Example

data unique_data;
  set original_data;
  by some_variable;
  if first.some_variable;
run;
PROC SORT Example

proc sort data=original_data out=unique_data nodup;
  by some_variable;
run;

39. Maximum Dataset Size in SAS.

The maximum size of a SAS dataset is limited by the system's available memory and resources. While older versions had limitations on the number of variables, current versions (9.1 and later) are more flexible and can handle a vast number of observations and variables.

40. Common Mistakes in SAS Programming.

  • Missing semicolons (;): Each SAS statement must end with a semicolon.
  • Ignoring the log: The SAS log provides critical information about errors and warnings.
  • Poor debugging practices: Using debugging tools effectively is essential.
  • Insufficient or missing comments: Comments make code easier to understand and maintain.