SAS Programming Guidelines Interview Questions You'll Most .....

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SAS Programming Guidelines Interview Questions You'll Most Likely Be Asked is a perfect companion to stand ahead above the rest in today’s competitive job market. Rather than going through comprehensive, textbook-sized reference guides, this book includes only the information required immediately for job search to build an IT career. This book puts the interviewee in the driver's seat and helps them steer their way to impress the interviewer.

Table of Contents

1. Efficient SAS Programming
2. Memory Usage
3. Data Storage Space
4. Best Practices
5. Sorting Strategies
6. Samples
7. Using Indexes
8. Combining Data Vertically
9. Combining Data Horizontally
10. Lookup Tables
11. Formatting Data
12. Tracking Changes
HR Interview Questions

a) 215 SAS Programming Guidelines Interview Questions, Answers and proven Strategies for getting hired as an IT professional
b) Dozens of examples to respond to interview questions
c) 78 HR Questions with Answers and proven strategies to give specific, impressive, answers that help nail the interviews
d) 2 Aptitude Tests download available on

Sample from the book

(Below Questions and Answers are randomly taken from different pages of the book)

33: What compresses the data storage space required to store a dataset?


SAS programs comprise of many temporary datasets which holds information during the runtime. You can choose to hold the data permanently in one or more datasets depending upon the available space and program requirements. Ideally, you can save the space for datasets by reducing the number and size of datasets and by cleaning up the storage space of everything unnecessary. SAS uses compression algorithms to reduce the size of the datasets. The Compress = Yes or Binary option is used to compress the dataset. The Reuse = Yes is used when you want to reuse the space after compression. Compress = Yes is used with datasets that primarily contain character data. Compress = Binary is used with datasets that primarily contain numeric data.


34: How does the WHERE statement help in reducing data storage space?


The WHERE statement lets you remove all unnecessary observations or records being fetched into the dataset. When using the WHERE statement, only those records that satisfy the WHERE condition will be fetched by the dataset. So, it helps to filter the data being fetched thereby reducing the data storage space.


35: How do you clean up the storage space?


You can clean up the storage space by using the DATASETS or DELETE procedures. While using the PROC data sets method, you have to mention the library and then the data set to delete. When using the Proc Delete method, you have to mention the exact data to be deleted. The more popular version is to use the PROC data sets option. This makes sure that the temporary file created to hold the data is deleted as soon as it is not required anymore.


36: Explain Compress = System.


The Compress = System option is used to compress all DATAFILES created during a particular session. It is used as Option Compress = NO/YES/BINARY/CHAR. By default, Compress is set to No which means no compression. When you set it to Yes or Char, using the RLE algorithm, the trailing blanks and zeroes are trimmed off. It basically compresses the character data. The Binary option used with Compress runs a Ross Data Compression which uses a combination of RLE and sliding-window compression where in a dictionary of frequently used words or character patterns are stored. The dictionary assigns a number and replaces the phrase with that number on each occurrence. A map of these numbers and phrases are maintained separately. Thus, the main dataset is compressed.


37: I have a compressed dataset. I want to add an observation to it. Will it allow me to add the new observation? If yes, where will it be added?


Yes, you can add new observations to an already compressed dataset. The new observation will be added to the end of the existing list. This is because the descriptor of the dataset will rest after the last observation in the dataset. If there’s any observation deleted, it is not reused or tracked. Instead, the new observations are added at the end of the current dataset.

SAS Programming Guidelines Interview, SAS Programming Guidelines, Efficient SAS Programming, SAS Sorting Strategies, SAS Best Practices, SAS Programming Lookup Tables, Learning SAS Programming, HR Questions, Job Interview Questions, Vibrant Publishers

COM077000   COMPUTERS / Mathematical & Statistical Software
MAT029000   MATHEMATICS / Probability & Statistics / General
COM021030   COMPUTERS / Databases / Data Mining