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Question 1
When attributes are permanently assigned in a DATA step, what is retained?
Correct Answer:
The attributes stored in the data set descriptor
Explanation:
When attributes are permanently assigned in a DATA step, the correct answer pertains to the attributes that are stored in the data set descriptor. The data set descriptor contains essential information about the data set, including the variable names, data types, lengths, labels, and any formats or informats that have been applied to the variables. By assigning attributes in a DATA step and using statements like LENGTH, LABEL, and FORMAT, you are establishing certain characteristics for your variables that will persist in the resultant data set. This means that when you save the data set, those attributes are stored and will be available whenever you access the data set in the future. In contrast, the other options focus on more specific aspects that do not encompass the full range of attributes stored in the data set descriptor. For instance, variable names and numeric values alone do not convey the complete set of attributes, nor do they fully represent the comprehensive nature of the descriptor, making the correct answer particularly relevant and encompassing in this context.
Question 2
How is a 'black space' created under the column headers in a SAS report?
Correct Answer:
Using the HEADSKIP statement
Explanation:
The creation of a 'black space' under the column headers in a SAS report is achieved with the HEADSKIP statement. This statement is specifically designed to insert blank lines in the output, which can enhance the readability of the report by providing visual separation between the headers and the data that follows. The HEADLINE statement, while useful for drawing horizontal lines above the column headers, does not create a space beneath them. The RBREAK statement is utilized for summation or special display at the end of groups, often to separate sections of data but does not specifically address creating space under headers. The BREAK statement is similar in purpose to the RBREAK statement, but it functions mainly for in-group breaks and does not have capability to add space beneath column headers. Therefore, the function of the HEADSKIP statement is unique and essential for achieving the desired spacing effect in SAS output reports.
Question 3
Which DO loop will NOT execute for the values 2, 4, 6, 8?
Correct Answer:
DO i = 1 to 8 by 2;
Explanation:
The correct choice is based on understanding how DO loops work in SAS programming. The option that specifies a loop using the syntax "DO i = 1 to 8 by 2;" generates a sequence of numbers starting at 1 and increments by 2 until it exceeds 8. This results in the values 1, 3, 5, and 7 being produced. Therefore, this loop will not produce the values 2, 4, 6, and 8, making it the correct answer. The other options are designed to yield explicitly the values specified (2, 4, 6, and 8). The first option directly creates a list of these values within the DO loop statement. The second option defines multiples of 2 that directly correspond to 2, 4, 6, and 8. Since both of these loops will indeed execute and provide the values specified, they do not match the condition for exclusion. Thus, SAMPLEonly the third option fails to execute for the values 2, 4, 6, and 8 as requested in the question.
Question 4
How many tables are generated when the following SAS program is submitted: PROC FREQ DATA=work.schools; RUN;?
Correct Answer:
4 tables, one table for each variable
Explanation:
The correct answer indicates that four tables are generated, each corresponding to a separate variable in the dataset. When the PROC FREQ procedure is executed without specifying any additional parameters, SAS automatically calculates frequency distributions for all the variables present in the dataset. In a typical scenario, PROC FREQ creates a frequency table for each categorical variable within the dataset being analyzed. If there are character variables and numeric variables, PROC FREQ will generate one frequency table for each of the categorical variables it encounters. Assuming there are four categorical variables in the "work.schools" dataset, the procedure would create one table per variable, resulting in a total of four tables. This process allows users to easily examine how many times each level of each categorical variable appears in the dataset, thus providing necessary insights into the distribution of those variables. The other potential answers do not align with how PROC FREQ operates in SAS. For example, if no tables are created, it would imply that there are no variables to analyze, which is not the case here if we assume the dataset contains relevant variables. Similarly, the answer suggesting a single table or a specific number of single-variant tables fails to account for the nature of frequency tables generated by PROC FREQ across multiple categorical variables
Question 5
In the context of the SYMBOL statement, what does the 'i=' option specify?
Correct Answer:
Interpolation method
Explanation:
The 'i=' option within the SYMBOL statement in SAS is used to specify the interpolation method for the symbols in a plot. This means it controls how SAS draws the line segments between the data points. Different interpolation methods can affect the visual representation of the data, especially for time series or line plots where the connection between points is significant. For example, linear interpolation creates straight lines between points, while other methods may provide smoother curves or different visual effects. In contexts where data points might be sporadic or irregular, choosing the correct interpolation can enhance the clarity and usefulness of a graphical output. This attention to detail in graphical representation is crucial when presenting data to ensure that the information is conveyed accurately and effectively. Understanding other options: Symbol shape, line width, and color of symbols pertain to different characteristics in the SYMBOL statement. While they define how the symbols look in terms of their shape, size, and color, the 'i=' option is specifically focused on the method of connecting those symbols rather than their artistic attributes.
Question 1
Exam overview

About this Exam

The SAS Certified Specialist: Base Programming Using SAS 9.4 credential is the essential first step for professionals looking to prove their competency in SAS programming. This globally recognized certification validates your ability to read, write, and debug SAS programs, as well as manipulate and transform data to solve real-world problems. It is designed for entry-level data analysts, programmers, statistical modelers, and researchers who use SAS as their primary tool for data management and analysis. Achieving this certification demonstrates to employers that you have the practical, hands-on skills required to contribute immediately to data-driven projects.

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Additional Information

What the Course Entails and Exam Details

To succeed on this exam, candidates must possess a fundamental understanding of SAS programming concepts. The exam focuses on practical application, assessing your ability to import essential data files, manipulate and transform datasets using the SAS DATA step, handle data errors, and create basic reports using SAS procedures.

Key skills covered include:

  • Accessing and creating SAS data structures.

  • Importing and exporting data to and from Excel or delimited files.

  • Controlling data flow and logic within the DATA step.

  • Processing data using DO loops and SAS arrays.

  • Combining and merging SAS datasets.

  • Formatting and summarizing data.

  • Generating reports using PROC PRINT, PROC FREQ, PROC MEANS, and ODS.


What to Expect in the Final Exam

The actual certification exam (A00-231) is a performance-based assessment, meaning you will be asked to solve programming challenges within a live SAS environment. Candidates are expected to write, execute, and troubleshoot SAS code to answer the questions.

  • Format: Performance-based questions and multiple-choice items.

  • Duration: 135 minutes (2 hours and 15 minutes) are allocated to complete the exam.

  • Questions: The exam typically contains 40–45 questions.

  • Passing Score: The passing score is 725 (on a scale of 200–1000).

  • Environment: The exam provides access to SAS programming environments like SAS Studio or SAS Enterprise Guide to solve the performance-based tasks.


How to Study and Exam Centers

Preparation is key to passing this exam, especially since it requires hands-on programming. While this Practice Exam is an excellent tool, combining it with other resources is recommended.

Study Strategies:

  1. Hands-on Practice: This is crucial. Spend time writing SAS code for various tasks, including data importing, manipulation, and reporting. The more comfortable you are within the SAS environment, the better you will perform during the exam's timed challenges.

  2. Review the Content Guide: SAS provides a detailed exam content guide outlining every objective covered. Use this to ensure your studies are comprehensive.

  3. Use Practice Exams: Take timed practice tests to familiarize yourself with the question formats, assess your speed, and identify areas where you need further review.

  4. Review Official Documentation: The SAS documentation is a valuable reference for syntax and procedural details.

Exam Centers:

The SAS Base Programming Certification exam is administered globally through Pearson VUE. Candidates have two options for taking the exam:

  • Pearson VUE Testing Centers: You can register to take the exam in person at a physical Pearson VUE testing location available worldwide.

  • Online Proctored Exam: You can also take the exam from the comfort of your home or office using Pearson VUE’s OnVUE online proctoring service. This option requires a compatible computer, a reliable internet connection, and a private environment.

To register, you will need to create an account on the Pearson VUE website and schedule your preferred date and format (in-person or online).


Job Opportunities from the Course

Earning your SAS Base Programming Certification opens doors to numerous career opportunities across various industries, including healthcare, finance, research, and technology, that rely on data analysis.

Below are common job titles and career paths available to certified professionals:

  • SAS Programmer / Senior SAS Programmer

  • Statistical Programmer

  • Data Analyst / Junior Data Analyst

  • Statistical Analyst

  • Business Intelligence (BI) Analyst

  • Clinical Data Programmer (in the pharmaceutical/CRO industry)

  • Data Manager / Clinical Data Manager

  • Risk Analyst (in finance/insurance)

  • Marketing Analyst

  • Market Research Analyst

  • Research Associate (academia/public sector)

This certification often serves as a prerequisite or a major differentiator for entry-level and intermediate roles requiring SAS proficiency, often leading to roles that bridge the gap between technical data management and strategic business decision-making.


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