SAS Interview Questions and Answers for 5 years experience

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SAS Interview Questions and Answers (5 Years Experience)

  1. What are the different types of SAS datasets?

    • Answer: SAS datasets primarily come in two forms: SAS data sets (.sas7bdat) which are binary files and are efficient for storage and processing, and external files (e.g., CSV, Excel) which can be read into SAS for processing. Within SAS datasets, there's a distinction between observational data sets (rows representing observations, columns representing variables) and raw data sets. There are also differences based on how the data is stored (e.g., compressed, encrypted).

  2. Explain the difference between PROC MEANS and PROC SUMMARY.

    • Answer: Both PROC MEANS and PROC SUMMARY calculate descriptive statistics, but PROC MEANS offers more flexibility and options. PROC MEANS provides more detailed output, including things like standard error, confidence intervals, and various types of means (e.g., weighted means), while PROC SUMMARY is faster and more efficient for basic summary statistics like sum, mean, and standard deviation, especially with large datasets.

  3. How do you handle missing values in SAS?

    • Answer: SAS represents missing values with a special dot (.). Several methods handle them: using the MISSING statement in PROCs to define missing values, employing the IF-THEN-ELSE statements or data step functions (e.g., IFNA, COALESCE) to replace missing values with a specific value (e.g., mean, median, 0), using imputation techniques (e.g., mean imputation, regression imputation) to estimate missing values, and utilizing PROC MI for multiple imputation. The best approach depends on the context and the nature of the missing data (MCAR, MAR, MNAR).

  4. What are some common SAS functions you use frequently?

    • Answer: Commonly used functions include: INPUT (for reading data), PUT (for writing data), SUM, MEAN, MEDIAN, STD, MAX, MIN, INT, ROUND, LENGTH, SUBSTR, TRIM, UPCASE, LOWCASE, SCAN, DATEPART, YEAR, MONTH, DAY. The specific functions used depend heavily on the task.

  5. Describe your experience with SAS macros.

    • Answer: [Insert a detailed description of your experience with SAS macros, including your experience with macro variables, macro functions, %include statements, and the creation and use of complex macros to automate repetitive tasks and enhance code reusability.]

  6. Explain the concept of data steps in SAS.

    • Answer: Data steps are the fundamental building blocks of SAS programming. They allow you to read data, manipulate it, create new variables, and write it to a new dataset. A data step consists of a DATA statement, a series of statements to process data (assignments, conditional logic, loops), and a RUN statement. They are executed row by row, allowing efficient data transformation and creation.

  7. How do you perform data cleaning in SAS?

    • Answer: Data cleaning involves various steps: identifying and handling missing values (as mentioned above), identifying and correcting data inconsistencies (e.g., incorrect data types, outliers), removing duplicates, standardizing data formats (e.g., dates, currencies), and validating data against business rules. Techniques involve using PROC FREQ, PROC MEANS, PROC UNIVARIATE for exploratory analysis, and data step programming for data manipulation and cleaning.

  8. What is the difference between a WHERE statement and an IF-THEN-ELSE statement?

    • Answer: A WHERE statement filters data *before* a data step executes, reducing the amount of data processed. An IF-THEN-ELSE statement filters data *during* a data step's execution, offering more flexibility in conditional data manipulation. WHERE is more efficient for simple filtering, while IF-THEN-ELSE is better for complex conditional logic and data transformations.

  9. How do you create a new variable in a SAS data set?

    • Answer: New variables are created within a data step using assignment statements. For example: `DATA new_dataset; SET old_dataset; new_var = old_var1 + old_var2; RUN;` This code creates a new variable `new_var` based on the values of `old_var1` and `old_var2` from the `old_dataset`.

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