SAS Openings and Interview Questions

SAS Interview Questions

2011-06-04T19:05:00.001+05:30

Can you give some examples for MAPPING *6?

Here are some examples for SDTM mapping:
• Character variables defined as Numeric
• Numeric Variables defined as Character
• Variables collected without an obvious corresponding domain in the CDISC SDTM mapping. So must go into SUPPQUAL
• Several corporate modules that map to one corresponding domain in CDISC SDTM.
• Core SDTM is a subset of the existing corporate standards
• Vertical versus Horizontal structure, (e.g. Vitals)
• Dates – combining date and times; partial dates.
• Data collapsing issues e.g. Adverse Events and Concomitant Medications.
• Adverse Events maximum intensity
• Metadata needed to laboratory data standardization.

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What do you understand about SDTM and its importance?
SDTM stands for Standard data Tabulation Model, which defines a standard structure for study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA) 2.
In July 2004 the Clinical Data Interchange Standards Consortium (CDISC) published standards on the design and content of clinical trial tabulation data sets, known as the Study Data Tabulation Model (SDTM). According to the CDISC standard, there are four ways to represent a subject in a clinical study: tabulations, data listings, analysis datasets, and subject profiles6.

Before SDTM:

There are different names for each domain and domains don’t have a standard structure. There is no standard variables list for each and every domain.

Because of this FDA reviewers always had to take so much pain in understanding themselves with different data, domain names and name of the variable in each analysis dataset. Reviewers will have spent most of the valuable time in cleaning up the data into a standard format rather than reviewing the data for the accuracy. This process will delay the drug development process as such.

After SDTM:
There will be standard domain names and standard structure for each domain. There will be a list of standard variables and names for each and every dataset. Because of this, it will become easy to find and understand the data and reviewers will need less time to review the data than the data without SDTM standards. This process will improve the consistency in reviewing the data and it can be time efficient.

The purpose of creating SDTM domain data sets is to provide Case Report Tabulation (CRT) data FDA, in a standardized format. If we follow these standards it can greatly reduce the effort necessary for data mapping. Improper use of CDISC standards, such as using a valid domain or variable name incorrectly, can slow the metadata mapping process and should be avoided4.

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PROC CDISC for SDTM 3.1 Format 2?

Syntax The PROC CDISC syntax for CDISC SDTM is presented below. The DATA= parameter specifies the location of your SDTM conforming data source.PROC CDISC MODEL=SDTM;SDTM SDTMVersion = "3.1";DOMAINDATA DATA = results. AE DOMAIN = AE CATEGORY = EVENT;RUN;
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What are the capabilities of PROC CDISC 2?

PROC CDISC performs the following checks on domain content of the source:
Verifies that all required variables are present in the data set
Reports as an error any variables in the data set that are not defined in the domain
Reports a warning for any expected domain variables that are not in the data set
Notes any permitted domain variables that are not in the data set
Verifies that all domain variables are of the expected data type and proper length
Detects any domain variables that are assigned a controlled terminology specification by the domain and do not have a format assigned to them.

The procedure also performs the following checks on domain data content of the source on a per observation basis:

Verifies that all required variable fields do not contain missing values

Detects occurrences of expected variable fields that contain missing values
Detects the conformance of all ISO-8601 specification assigned values; including date, time, date time, duration, and interval types
Notes correctness of yes/no and yes/no/null responses,
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What are the different approaches for creating the SDTM 3?

There are 3 general approaches to create the SDTM datasets:
a) Build the SDTM entirely in the CDMS,
b) Build the SDTM entirely on the “back-end” in SAS,
c) or take a hybrid approach and build the SDTM partially in the CDMS and partially in SAS.

BUILD THE SDTM ENTIRELY IN THE CDMS
It is possible to build the SDTM entirely within the CDMS. If the CDMS allows for broad structural control of the underlying database, then you could build your eCRF or CRF based clinical database to SDTM standards.

Advantages:
• Your “raw” database is equivalent to your SDTM which provides the most elegant solution.
• Your clinical data management staff will be able to converse with end-users/sponsors about the data easily since your clinical data manager and the und-user/sponsor will both be looking at SDTM datasets.
• As soon as the CDMS database is built, the SDTM datasets are available.

Disadvantages:
• This approach may be cost prohibitive. Forcing the CDMS to create the SDTM structures may simply be too cumbersome to do efficiently.
• Forcing the CDMS to adapt to the SDTM may cause problems with the operation of the CDMS which could reduce data quality.

BUILD THE SDTM ENTIRELY ON THE “BACK-END” IN SAS
Assuming that SAS is not your CDMS solution, another approach is to take the clinical data from your CDMS and manipulate it into the SDTM with SAS programming.

Advantages:
• The great flexibility of SAS will let you transform any proprietary CDMS structure into the SDTM. You do not have to work around the rigid constraints of the CDMS.
• Changes could be made to the SDTM conversion without disturbing clinical data management processes.
• The CDMS is allowed to do what it does best which is to enter, manage, and clean data.

Disadvantages: • There would be additional cost to transform the data from your typical CDMS structure into the SDTM.
Specifications, programming, and validation of the SAS programming transformation would be required.
• Once the CDMS database is up, there would then be a subsequent delay while the SDTM is created in SAS.

This delay would slow down the production of analysis datasets and reporting. This assumes that you follow the linear progression of CDMS -> SDTM -> analysis datasets (ADaM).

• Since the SDTM is a derivation of the “raw” data, there could be errors in translation from the “raw” CDMS data to the SDTM.
• Your clinical data management staff may be at a disadvantage when speaking with end-users/sponsors about the data since the data manager will likely be looking at the CDMS data and the sponsor will see SDTM data.

BUILD THE SDTM USING A HYBRID APPROACH
Again, assuming that SAS is not your CDMS solution, you could build some of the SDTM within the confines of the CDMS and do the rest of the work in SAS. There are things that could be done easily in the CDMS such as naming data tables the same as SDTM domains, using SDTM variable names in the CTMS, and performing simple derivations (such as age) in the CDMS. More complex SDTM derivations and manipulations can then be performed in SAS.

Advantages:
• The changes to the CDMS are easy to implement.
• The SDTM conversions to be done in SAS are manageable and much can be automated.

Disadvantages:
• There would still be some additional cost needed to transform the data from the SDTM-like CDMS structure into the SDTM. Specifications, programming, and validation of the transformation would be required.
• There would be some delay while the SDTM-like CDMS data is converted to the SDTM.
• Your clinical data management staff may still have a slight disadvantage when speaking with endusers/ sponsors about the data since the clinical data manager will be looking at the SDTM-like data and the sponsor will see the true SDTM data.

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What do you know about SDTM domains?

basic understanding of the SDTM domains, their structure and their interrelations is vital to determining which domains you need to create and in assessing the level to which your existing data is compliant. The SDTM consists of a set of clinical data file specifications and underlying guidelines. These different file structures are referred to as domains. Each domain is designed to contain a particular type of data associated with clinical trials, such as demographics, vital signs or adverse events.

The CDISC SDTM Implementation Guide provides specifications for 30 domains. The SDTM domains are divided into six classes.

The 21 clinical data domains are contained in three of these classes:
Interventions,
Events and
Findings.

The trial design class contains seven domains and the special-purpose class contains two domains (Demographics and Comments).

The trial design domains provide the reviewer with information on the criteria, structure and scheduled events of a clinical trail. The only required domain is demographics.

There are two other special purpose relationship data sets, the Supplemental Qualifiers (SUPPQUAL) data set and the Relate Records (RELREC) data set. SUPPQUAL is a highly normalized data set that allows you to store virtually any type of information related to one of the domain data sets. SUPPQUAL domain also accommodates variables longer than 200, the Ist 200 characters should be stored in the domain variable and the remaining should be stored in it5.

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What are the general guidelines to SDTM variables?

Each of the SDTM domains has a collection of variables associated with it.
There are five roles that a variable can have:
Identifier,
Topic,
Timing,
Qualifier,
and for trial design domains,

Rule. Using lab data as an example, the subject ID, domain ID and sequence (e.g. visit) are identifiers.
The name of the lab parameter is the topic,
the date and time of sample collection are timing variables,
the result is a result qualifier and the variable containing the units is a variable qualifier.

Variables that are common across domains include the basic identifiers study ID (STUDYID), a two-character domain ID (DOMAIN) and unique subject ID (USUBJID).

In studies with multiple sites that are allowed to assign their own subject identifiers, the site ID and the subject ID must be combined to form USUBJID.
Prefixing a standard variable name fragment with the two-character domain ID generally forms all other variable names.

The SDTM specifications do not require all of the variables associated with a domain to be included in a submission. In regard to complying with the SDTM standards, the implementation guide specifies each variable as being included in one of three categories:

Required, Expected, and Permitted4.

REQUIRED – These variables are necessary for the proper functioning of standard software tools used by reviewers. They must be included in the data set structure and should not have a missing value for any observation.

EXPECTED – These variables must be included in the data set structure; however it is permissible to have missing values.

PERMISSIBLE – These variables are not a required part of the domain and they should not be included in the data set structure if the information they were designed to contain was not collected.

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Can you tell me more About SDTM Domains5?

SDTM Domains are grouped by classes, which is useful for producing more meaningful relational schemas. Consider the following domain classes and their respective domains.

• Special Purpose Class – Pertains to unique domains concerning detailed information about the subjects in a study.

Demography (DM), Comments (CM)

• Findings Class – Collected information resulting from a planned evaluation to address specific questions about the subject, such as whether a subject is suitable to participate or continue in a study.

Electrocardiogram (EG)
Inclusion / Exclusion (IE)
Lab Results (LB)
Physical Examination (PE)
Questionnaire (QS)
Subject Characteristics (SC)
Vital Signs (VS)

• Events Class – Incidents independent of the study that happen to the subject during the lifetime of the study.

Adverse Events (AE)
Patient Disposition (DS)
Medical History (MH)

• Interventions Class – Treatments and procedures that are intentionally administered to the subject, such as treatment coincident with the study period, per protocol, or self-administered (e.g., alcohol and tobacco use).

Concomitant Medications (CM)
Exposure to Treatment Drug (EX)
Substance Usage (SU)

• Trial Design Class – Information about the design of the clinical trial (e.g., crossover trial, treatment arms) including information about the subjects with respect to treatment and visits.

Subject Elements (SE)
Subject Visits (SV)
Trial Arms (TA)
Trial Elements (TE)
Trial Inclusion / Exclusion Criteria (TI)

Trial Visits (TV)

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Can you tell me how to do the Mapping for existing Domains?

First step is the comparison of metadata with the SDTM domain metadata. If the data getting from the data management is in somewhat compliance to SDTM metadata, use automated mapping as the Ist step.

If the data management metadata is not in compliance with SDTM then avoid auto mapping. So do manual mapping the datasets to SDTM datasets and the mapping each variable to appropriate domain.

The whole process of mapping include: *Read in the corporate data standards into a database table.
• Assign a CDISC domain prefix to each database module.
• Attach a combo box containing the SDTM variable for the selected domain to a new mapping variable field.
• Search each module, and within each module select the most appropriate CDISC variable.
•Then search for variables mapped to the wrong type Character not equal to Character; Numeric not equal to Numeric.
• Review the mapping to see if any conflicts are resolvable by mapping to a more appropriate variable.
• We need to verify that the mapped variable is appropriate for each role.
• Then finally we have to ensure all ‘required’ variables are present in the domain6.

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What do you know about SDTM Implementation Guide, Have you used it, if you have can you tell me which version you have used so far?

SDTM Implementation guide provides documentation on metadata (data of data) for the domain datasets that includes filename, variable names, type of variables and its labels etc. I have used SDTM implementation guide versions 3.1.1/3.1.2

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Can you identify which variables should we have to include in each domain?

SDTM implementation guide V 3.1.1/V 3.1.2 specifies each variable is being included in one of the 3 types.
REQUIRED –They must be included in the data set structure and should not have a missing value for any observation.

EXPECTED – These variables must be included in the data set; however it is permissible to have missing values.

PERMISSIBLE – These variables are not a required part of the domain and they should not be included in the data set structure if the information they were designed to contain was not collected.
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Explain the Process of SDTM Mapping?

A list of basic variable mappings is given below.

DIRECT: a CDM variable is copied directly to a domain variable without any changes other than assigning the CDISC standard label.
RENAME: only the variable name and label may change but the contents remain the same.
STANDARDIZE: mapping reported values to standard units or standard terminology
REFORMAT: the actual value being represented does not change, only the format in which is stored changes, such as converting a SAS date to an ISO8601 format character string.
COMBINING: directly combining two or more CDM variables to form a single SDTM variable.
SPLITTING: a CDM variable is divided into two or more SDTM variables.
DERIVATION: creating a domain variable based on a computation, algorithm, series of logic rules or decoding using one or more CDM variables.
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What are the Common Issues in Mapping Dummy corporate standards to CDISC (SDTM) Standards?

• Character variables defined as Numeric
• Numeric Variables defined as Character
• Variables collected without an obvious corresponding domain in the CDISC SDTM mapping. So must go into SUPPQUAL
• Several corporate modules that map to one corresponding domain in CDISC SDTM.
• Dictionary codes not in SDTM parent module, so if needed must be collected in SUPPQUAL.
• Core SDTM is a subset of the existing corporate standards
• Different structure of Lab CDISC Domain e.g. baseline flag.
• Vertical versus Horizontal structure, (e.g. Vitals)
• Additional Metadata needed to describe the source in SUPPQUAL
• Dates – combining date and times; partial dates.
• Data collapsing issues e.g. Adverse Events and Concomitant Medications.
• Adverse Events maximum intensity
• Metadata needed to laboratory data standardization.

The Analysis Data Model describes the general structure, metadata, and content typically found in Analysis Datasets and accompanying documentation. The three types of metadata associated with analysis datasets (analysis dataset metadata, analysis variable metadata, and analysis results metadata) are described and examples provided. (source:CDISC Analysis Data Model: Version 2.0)
Analysis datasets (AD) are typically developed from the collected clinical trial data and used to create statistical summaries of efficacy and safety data. These AD’s are characterized by the creation of derived analysis variables and/or records. These derived data may represent a statistical calculation of an important outcome measure, such as change from baseline, or may represent the last observation for a subject while under therapy. As such, these datasets are one of the types of data sent to the regulatory agency such as FDA.

The CDISC Analysis Data Model (ADaM) defines a standard for Analysis Dataset’s to be submitted to the regulatory agency. This provides a clear content, source, and quality of the datasets submitted in support of the statistical analysis performed by the sponsor.

In ADaM, the descriptions of the AD’s build on the nomenclature of the SDTM with the addition of attributes, variables and data structures needed for statistical analyses. To achieve the principle of clear and unambiguous communication relies on clear AD documentation. This documentation provides the link between the general description of the analysis found in the protocol or statistical analysis plan and the source data.

SAS Interview Questions

2011-06-04T18:54:00.003+05:30

1) What do you know about CDISC and its standards?
CDISC stands for Clinical Data Interchange Standards Consortium and it is developed keeping in mind to bring great deal of efficiency in the entire drug development process. CDISC brings efficiency to the entire drug development process by improving the data quality and speed-up the whole drug development process and to do that CDISC developed a series of standards, which include Operation data Model (ODM), Study data Tabulation Model (SDTM) and the Analysis Data Model ADaM).

2) Why people these days are more talking about CDSIC and what advantages it brings to the Pharmaceutical Industry?
A) Generally speaking, Only about 30% of programming time is used to generate statistical results with SAS®, and the rest of programming time is used to familiarize data structure, check data accuracy, and tabulate/list raw data and statistical results into certain formats. This non-statistical programming time will be significantly reduced after implementing the CDISC standards.

3) What are the challenges as SAS programmer you think you will face when you first implement CDISC standards in you company?
A) With the new requirements of electronic submission, CRT datasets need to conform to a set of standards for facilitating reviewing process. They no longer are created solely for programmers convenient. SDS will be treated as specifications of datasets to be submitted, potentially as reference of CRF design. Therefore, statistical programming may need to start from this common ground. All existing programs/macros may also need to be remapped based on CDISC so one can take advantage to validate submission information by using tools which reviewer may use for reviewing and to accelerate reviewing process without providing unnecessary data, tables and listings. With the new requirements from updating electronic submission and CDISC implementation, understanding only SAS® may not be good enough to fulfill for final deliverables. It is a time to expand and enhance the job skills from various aspects under new change so that SAS® programmers can take a competitive advantage, and continue to play a main role in both statistical analysis and reporting for drug development.

Get Ready for BI Market

2010-06-01T16:21:00.000+05:30

Hi All,

Be Ready For BI Market,Becz in Future there will be more Demand for BI Professionals. So instead of Learning at that time be trained now itself.

ALL THE BEST

SAS Market is in full Josh

2010-04-29T16:23:00.000+05:30

Hi,

This week there are multiple openings in sas.
Especially in TCS , If you have any friends in TCS,
Ask them to send your CV. this week end there are interview.
Hurry up , grab your oppertunity

ALL THE BEST

Efficient SAS Programming Techniques For Programming Time

2010-04-24T10:33:00.004+05:30

1) Use the SQL procedure for code simplification.
2) Use procedures whenever possible.
3) Document programs and routines with comments.
4) Utilize macros for redundant code.
5) Code for unknown data values.
6) Assign descriptive and meaningful variable names.
7) Store formats and labels with the SAS datasets that
use them.
8) Test program code using complete test data

Efficient SAS Programming Techniques For I/O

2010-04-24T10:32:00.003+05:30

1) Read only data that is needed.
2) Use WHERE statements to subset data.
3) Use data compression for large datasets.
4) Use the DATASETS procedure COPY statement to
copy datasets with indexes.
5) Use the SQL procedure to consolidate code.
6) Store data in SAS datasets, not external files.
7) Perform data subsets early and at same time.
8) Use KEEP or DROP statements to retain desired
variables.

Efficient SAS Programming Techniques For Data Storage

2010-04-24T10:31:00.003+05:30

1) Use KEEP or DROP statements to retain desired
variables.
2) Use LENGTH statements to reduce variable size.
3) Use data compression strategies.
4) Create character variables as much as possible.
5) Use DATA _NULL_ steps for processing null
datasets.

Efficient SAS Programming Techniques for CPU Time

2010-04-24T10:30:00.002+05:30

1) Use KEEP or DROP statements to retain desired
variables.
2) Create and use indexes with large datasets.
3) Utilize macros for redundant code.
4) Use IF-THEN/ELSE statements to process data.
5) Use the DATASETS procedure COPY statement to
copy datasets with indexes.
6) Use the SQL procedure to consolidate the number of
steps.
7) Turn off the Macro facility when not needed.
8) Avoid unnecessary sorting - plan its use.
9) Use CLASS statements in procedures.
10)Use the Stored Program Facility for complex DATA
steps.

Please do Comments

Top Ten Reasons To go for PROC SQL

2010-04-24T10:27:00.000+05:30

In this , I will go over some usages of PROC SQL, which either simplify SAS programming or produce output that is not available in the non-SQL part of SAS.

1.Joining tables
The advantages are:
• No sorting needed.
• Two tables can join on different variable names.

2.Build macro value list
The following SQL example assigns a whole column of values to a
macro variable.This can be useful in two situations:
• Outputting information to title/footnote statements using macro variables
when you don’t know the new values (department code) in advance.
• Using the macro variable as the value for your IN statement

3.Access other databases
PROC SQL is the only way you can join a SAS table and an Oracle table.

4.Textwrapping
When you have a long character variable (such as a COMMENT field in
the questionnaire), and you want to print the values using PROC PRINT,
you will get the warning message:
WARNING: Data too long for column "COMMENT"; truncated to 124 characters
to fit.A simple solution is to use PROC SQL with the flow option.
An alternative would be to use PROC REPORT.
(Note: Flow=30 has an effect on all character variables.)

5.Count frequencies
Using PROC SQL, you can quickly count non-missing values for several
variables and output the result on one line. (PROC FREQ would produce
several output tables with the output sometimes continuing on to the next page.)

6.Matching multiple tables at different levels

7.Insert records to a table

8.COALESCE function
The COALESCE function will pick the first non-missing value.

9.Summarize data
You can use SQL functions to summarize data. PROC SQL is more intuitive
than PROC MEANS or PROC SUMMARY.

10.Fuzzy merge
Fuzzy merge is the process of matching records where the condition of a
match is based on close-but-not equivalent condition.

New SAS Openings in India

2010-04-21T14:29:00.001+05:30

Junior Clinical SAS/ Statistical Programmer TECH OBSERVER 0-3 yrs

Company Name :TECH OBSERVER
Website :http://www.tech-observer.com
Executive Name :Aman and Prankur
Email Address :prankur@tech-observer.com
Telephone :9811477126,9999019521

For Detailed job Description

All The Best

For general intrerview questions

2010-04-21T13:51:00.004+05:30

Click the below links for sas Faqs

click here for general interview questions
For sample format of sas certification
For sas interview questions

How can you test data , whether the data has obs and find the values in column?

2010-04-21T13:25:00.006+05:30

A.By using Proc freq, we can tell that data contains data or not and to find the values in a perticular column name.

Proc freq data=sashelp.class;
table age / nocum missing;
run;

the result of this procedure gives

sex--freq---per
M----10-----52.63
F-----9-----47.37

if there is missing values then at last it will display the number of missing values.

and also by proc means we can find how many missing and non missing values

proc means data=sashelp.class n nmiss;
run;

the output looks like

variable label n nmiss
sex sex 19 0

where n is total no of non missing values and nmiss is missing values.

for more detail click here

How to handle errors in sas?

2010-04-20T10:44:00.002+05:30

SAS is a very User Friendly tool. By default it has 3 window
1.Enhanced Editor
2.Log Window
3.Output window
and also u find explorer and result window in left side.

when ever error occurred in program , sas system throws errors into log window.and also it will tell the expected variables or symbols in place of error.
by just seeing the errors you have to correct the program.

for example when you are using macro debugging options,
in that you are calling a macro that is not there in system.
then the sas system displays error like this.
"Apparent invocation of macro not resolved".

How can You Delete Duplicate Variables in SAS Data-step?

2010-04-19T18:29:00.002+05:30

A. The Answer is very simple,By using First.Variable and last.Variable we can delete duplicate values in SAS Data step.

Example : suppose sample dataset having 2 columns one is number and second one is name
if number has duplicate values,and we have to take first of all duplicate values then go for first.variable.

data sample1;
set sample;
by number;
if first.number=1;
run;

same for last.variable for taking last value in duplicate values

Note:For using first.variable or last.variable , use must and should BY Statement before the first or last variables.

How can You Delete Duplicate Variables in SAS Data-step?

2010-04-19T18:04:00.001+05:30

Very Urgent Openings in SAS

2010-04-19T18:03:00.000+05:30

Associate - Operations Systems Support HSBC Operations &
Processing Enterprise (India) Pvt Ltd

Good opportunities for freshers.

Company Name:HSBC Operations & Processing Enterprise (India) Pvt Ltd
Website:Not Mentioned
Executive Name:Anupama Sindhia
Address:Not Mentioned
Telephone:25252000

interested Candidates can walk in for an interview on 20 Apr 10 anytime
from 09.00 am - 12.00 pm
Venue: #30, Rajaji Salai, Parrys, Chennai-01

Hurry Call To Number which they have given and attend interview.
ALL THE BEST.

sas openings

2010-04-16T14:49:00.000+05:30

Analyst/Sr. Analyst - Risk (Credit Card) Global Financial Services Leader 1-5 yrs
Company Name :Global Financial Services Leader
Executive Name:Consultant, Analytics, Bangalore
Address :Not Mentioned
Email Address :arindam.ghosh@staffersindia.com
Telephone :Not Mentioned

Multiple sas openings in India

2010-04-15T15:39:00.000+05:30

Market Research Analyst 0-3 yrs
Company Name :Bridge2Tech Consultancy Private Limited
Website :http://bridge2tech.com
Address :Not Mentioned
Email Address:resumes@bridge2tech.com

Company Name :Global Financial Services Leader
Website :Not Mentioned
Executive Name:Consultant, Analytics, Bangalore
Address :Not Mentioned
Email Address :arindam.ghosh@staffersindia.com
Analyst/Sr. Analyst - Risk (Credit Card) 1- 5 yrs

Senior Manager - IT Leading General Insurance Company 3-5 yrs
Company Name :Leading General Insurance Company
Website :Not Mentioned
Executive Name:Team Talent Discoveri
Address :Not Mentioned
Email Address :enquiries@talentdiscoveri.com
Telephone :Not Mentioned
Reference ID :IT//GI//MIS//MUM

Urgently Required Sr. SAS BI Sr. Developer,Pune Survik Software Ltd 3-6 yrs
Company Name :Survik Software Ltd
Website :https://www.survik.com
Executive Name:Nikita Mehrotra
Address :Survik Software Ltd
2nd Floor, Sapphire Chambers Opp Food Bazar
Baner Road
PUNE,Maharashtra,India 410401
Email Address :nikita.mehrotra@survik.com
Telephone :08975751396
Reference ID :SAS

SAS / SPSS analytics Sr. positions.. IT Giant 12-22 yrs
Executive Name:Rupali
Address :Not Mentioned
Email Address :rupali.patkar@naukri.com
Reference ID :RP Analytics SPSS

Risk Manager (Enterprise / Operational Risk Management)
Asterion Consulting Pvt Ltd (Client is Top IT Company) 2-7 yrs
Company Name :Asterion Consulting Pvt Ltd (Client is Top IT Company)
Website :Not Mentioned
Executive Name:Vignesh Rajan
Address :Not Mentioned
Email Address :vignesh@asterioninc.com
Telephone :080-42556814
Reference ID :RiskManager_Bangalore

BI Testing Engineer 3i Infotech Limited 3-5 yrs
Company Name :3i Infotech Limited
Email Address :opportunities@3i-infotech.com
Keywords: SAS, ETL / BI, Automated Testing, Testing, Oracle, PL / SQL, CRM

ALL THE BEST

some sas interview questions with answers

2010-04-15T13:54:00.001+05:30

1. How do you connect sas with oracle?
A. By using any of the two ways
1.By Libname Statement
2.By Sql Pass Thru Facility

first we will discuss about Libname statement
Libname oracle oralib username="oracle username" pwd="password of oracle"
Path="path of serverside oracle" schema="name of schema";

In this oracle is name of the database you want to connect
Oralib is reference name of database and while using oracle in local computer
no need of path option in libname statement.

secondly we will discuss Sql pass thru facility
Proc Sql;
connect to oracle
(username="username" password="password" path="path");
create table datasetname as
select * from connection to oracle
(select * from dept );
quit;

here datasetname is nothing but new sas dataset name
dept is oracle table name.

New SAS Openings in India

2010-04-13T19:41:00.000+05:30

Minimum 2 years proficiency required in Base SAS, SAS Macro and SAS Graph of which at least 1 years experience in the pharmaceutical industry or a clinical research organization.
Contact Vineeta
Telephone 0120-3009058
Email vineeta.kandari@signaturestaffindia.com
Website http://www.signaturestaffindia.com
Job Posted 13 Apr
Reference SAS prog/CRO

Some Common Interview Questions

2010-04-13T12:14:00.000+05:30

1. what is sas and where it is used?
2. Is sas is a tool or programmin language or data storeage or to build applications?
3. how sas code will execute when click on submit button?
4. what do you meant by PDV?
5. what is the difference between PDV and retain statement?
6. what are infile statement options?
7. what is difference between informats and formats?
8. what is put function and put statement?
9. how do you change yearcuttoff option in sas?
10. how do you create permanent libraries in sas?
11. how do you restrict observations when you are extracting from external
file or other dataset?
12. what are all procedures that you have used in your project.
13. what is the use of proc dataset?
14. what are the output procedures in sas?
15. how will you delete duplicate values in sas?
16. what is the difference between nodup and nodupkey in proc sort?

17. how many ways will you create macro variables?
18. how will you do arthametic operations for numeric and float variables;
19. what is the difference b/w call symput and call symget in macros?
20. how do you resolve macro variable ?
21. what are the sas macro debuging options?
22. how do you call macro?

23. how do you connect database to your sas program?
by two ways 1.by libname 2. by proc sql ?
24. how many types of joins in sas/sql?
25. what is full join and self join?
26. which one is more effective, merge statement in base sas and joins
in sas/sql?why?
27. what is the coalesec function in sql?
28. what is the proc dbload in sas?for what it is used?

SAS Interview Questions

2010-04-13T11:55:00.000+05:30

1. What SAS statements would you code to read an external raw data file to a DATA step?
2. How do you read in the variables that you need?
3. Are you familiar with special input delimiters? How are they used?

4. If reading a variable length file with fixed input, how would you prevent SAS from reading the next record if the last variable didn’t have a value?
5. What is the difference between an informat and a format? Name three informats or formats.
6. Name and describe three SAS functions that you have used, if any?
7. How would you code the criteria to restrict the output to be produced?
8. What is the purpose of the trailing @? The @@? How would you use them?
9. Under what circumstances would you code a SELECT construct instead of IF statements?
10. What statement do you code to tell SAS that it is to write to an external file? What statement do you code to write the record to the file?
11. If reading an external file to produce an external file, what is the shortcut to write that record without coding every single variable on the record?
12. If you’re not wanting any SAS output from a data step, how would you code the data statement to prevent SAS from producing a set?
13. What is the one statement to set the criteria of data that can be coded in any step?
14. Have you ever linked SAS code? If so, describe the link and any required statements used to either process the code or the step itself.
15. How would you include common or reuse code to be processed along with your statements?
16. When looking for data contained in a character string of 150 bytes, which function is the best to locate that data: scan, index, or indexc?
17. If you have a data set that contains 100 variables, but you need only five of those, what is the code to force SAS to use only those variable?
18. Code a PROC SORT on a data set containing State, District and County as the primary variables, along with several numeric variables.
19. How would you delete duplicate observations?
20. How would you delete observations with duplicate keys?
21. How would you code a merge that will keep only the observations that have matches from both sets.
22. How would you code a merge that will write the matches of both to one data set, the non-matches from the left-most data set to a second data set, and the non-matches of the right-most data set to a third data set.
23. What is the Program Data Vector (PDV)? What are its functions?
24. Does SAS ‘Translate’ (compile) or does it ‘Interpret’? Explain.
25. At compile time when a SAS data set is read, what items are created?
26. Name statements that are recognized at compile time only?
27. Identify statements whose placement in the DATA step is critical.
28. Name statements that function at both compile and execution time.
29. Name statements that are execution only.
30. In the flow of DATA step processing, what is the first action in a typical DATA Step?
31. What is _n_?

ALL THE BEST

SAS Openings in melstar for client side

2010-04-13T11:52:00.000+05:30

SAS BI Sr. Developers Requirement with Melstar at Pune - Client side

experience 5 years
Email sbhadke@melstar.com
Website http://www.melstar.com
Job Posted 12 Apr
client Wipro(not sure)
Reference Sr SAS BI / Pune / Immediate Joining / 5 yrs
Contact Sachin
Melstar Information Technologies Ltd
Melstar House,G-4, MIDC, Cross Rd A Andheri East,
Mumbai 400093

ALL THE BEST

Multiple SAS Openings

2010-04-13T11:46:00.000+05:30

Hi,

Many sas openings are there through out india.
the companies that are hiring sas programmers are..

1) G.E(hyderabad and Bangalore).
2) T.C.S(mumbai).
3) Accenture(noida,mumbai,bangalore).
4) H.s.b.c (kolkatta).
5) paroxal (hyderabad).
6) wipro info tech (mumbai).
7) Zensar (pune)
8) L & T finance (mumbai)
9) novartis (hyderabad)
10) torry harris (Bangalore)
11) First Indian Corperation (bangalore

Guys hurry up , if you have any reference farword your profile to these companies.

ALL THE BEST

2010-04-12T13:35:00.001+05:30