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SAS Certified Data Scientist: Complete Certification, Exam and Preparation Guide

See what SAS Data Scientist tests, what it takes, and whether it fits your goals

Capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work. Examine the SAS Data Scientist assessment, preparation demands, pricing, prerequisites, renewal expectations, and skills it can demonstrate. Compare the credential with adjacent options from SAS before deciding whether it belongs in your professional development plan.

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Credential overview

SAS Certified Data Scientist: What the certification covers and who it suits

SAS Certified Data Scientist is a capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

SAS Certified Data Scientist validates end-to-end data-science capability by combining advanced SAS programming with professional AI and machine-learning knowledge. Candidates develop the broad workflow perspective needed to turn data into evaluated, explainable analytic solutions.

Data scienceMachine learningSAS programmingAnalyticsSAS Viya

Who should take it

Choose this credential if you already have meaningful SAS and analytics experience and want a capstone data-science certification. It is appropriate for practitioners ready to demonstrate that they can connect programming, modeling, and decision support in one professional workflow.

Best for

This certification suits experienced SAS programmers, analysts, data scientists, analytics developers, quantitative professionals, and practitioners moving into end-to-end data-science work. It is most relevant for candidates who already have strong individual skills and want to demonstrate that they can connect them across a complete analytic lifecycle.

Why it matters

SAS Certified Data Scientist can demonstrate broad professional readiness for SAS-based data-science work. It is valuable for candidates who need to show both technical depth and workflow judgment. Its strongest evidence is a portfolio of well-scoped projects, clear reasoning, and outcomes that matter to a domain or organization.

Requirements

Candidates should have substantial experience with SAS programming, data preparation, statistics, predictive modeling, and analytic communication. Familiarity with SAS Viya and machine-learning workflows is valuable. Preparation should include full projects that begin with a problem and data, then progress through modeling, evaluation, explanation, and operational considerations.

Best fit

Who SAS Certified Data Scientist is best suited for

This certification suits experienced SAS programmers, analysts, data scientists, analytics developers, quantitative professionals, and practitioners moving into end-to-end data-science work. It is most relevant for candidates who already have strong individual skills and want to demonstrate that they can connect them across a complete analytic lifecycle.

Who should take it

Choose this credential if you already have meaningful SAS and analytics experience and want a capstone data-science certification. It is appropriate for practitioners ready to demonstrate that they can connect programming, modeling, and decision support in one professional workflow.

Best for

This certification suits experienced SAS programmers, analysts, data scientists, analytics developers, quantitative professionals, and practitioners moving into end-to-end data-science work. It is most relevant for candidates who already have strong individual skills and want to demonstrate that they can connect them across a complete analytic lifecycle.

Career value

Career value of SAS Certified Data Scientist

This credential supports data scientist, senior data analyst, analytics developer, machine-learning practitioner, quantitative consultant, and SAS analytics leadership paths. It can signal broad capability, while a strong body of real project work and domain expertise remain essential.

SAS Certified Data Scientist can demonstrate broad professional readiness for SAS-based data-science work. It is valuable for candidates who need to show both technical depth and workflow judgment. Its strongest evidence is a portfolio of well-scoped projects, clear reasoning, and outcomes that matter to a domain or organization.

Learning outcomes

SAS Certified Data Scientist: Skills and learning outcomes the certification is designed to validate

The value of SAS Certified Data Scientist depends on what you can do with the knowledge it assesses. Connect its learning outcomes to real decisions, tools, workflows, and problems, and identify where additional hands-on experience is needed beyond exam preparation.

  • Prepare and manage data for advanced analytic work
  • Apply programming and machine-learning capabilities to real problems
  • Evaluate and compare analytic solutions with appropriate criteria
  • Explain results, limitations, and recommendations clearly
  • Connect technical data-science work with organizational use and governance

Tags and keywords

Certification tags and search topics

Data scienceMachine learningSAS programmingAnalyticsSAS ViyaSAS Certified Data ScientistSAS data science certificationSAS machine learning professionalend to end data science SASadvanced SAS analyticsSAS Viya data scientist

Reference

Quick facts

Provider
SAS
Code
SAS-DS
Level
Expert
Credential type
Professional certification
Active exams
4
Known price
$180
Study time
440-760h
Last verified
Sep 8, 2026
Official page

Provider

SAS

Exam details

SAS Certified Data Scientist: Exam structure and assessed capability

The SAS Certified Data Scientist exam turns the credential’s published objectives into an assessment of knowledge and judgment. Review the tested topics, question or task style, delivery method, and any practical emphasis so your preparation reflects how the exam actually asks you to perform.

A00-232

Advanced Programming Using SAS 9.4

Performance-based programming tasks

Official exam
Type
Practical
Delivery
Both
Duration
125 min

Exam sections

01

SAS

The sas area tests whether a candidate can move from recognition to correct action. It includes the reasoning, workflow awareness, and failure analysis needed when working with capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall Advanced Programming Using SAS 9.4 format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Work through one straightforward and one ambiguous example of sas. For the ambiguous case, state the assumptions you need, choose an approach, and describe how you would verify that choice.

02

Combining

Within Advanced Programming Using SAS 9.4, combining is treated as an applied capability rather than an isolated definition. Candidates should be ready to interpret context, identify an appropriate next step, and account for the operational goals behind capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Candidates may encounter combining through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Create a comparison sheet for the main options, commands, controls, or methods associated with combining. Test the distinctions against realistic cases so similar-looking choices do not become guesswork.

03

Programming

Advanced Programming Using SAS 9.4 examines how candidates understand and apply programming within the wider credential scope. This area connects core concepts to the decisions, dependencies, and consequences practitioners encounter when carrying out the work described by capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall Advanced Programming Using SAS 9.4 format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Map programming to the preceding and following stages of the real workflow. This exposes dependencies that isolated flashcards miss and makes it easier to reason through unfamiliar combinations on assessment day.

04

Capstone Combining Advanced SAS Programming With AI

This area concentrates on capstone combining advanced sas programming with ai as it appears in realistic tasks and scenarios. Candidates need to recognize the relevant inputs, choose a defensible approach, and understand how the result supports capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Candidates may encounter capstone combining advanced sas programming with ai through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Simulate the constraints of Advanced Programming Using SAS 9.4 while practising capstone combining advanced sas programming with ai. Limit references, capture evidence as you work, and reserve time to check completeness so technique and exam execution improve together.

A00-406

SAS Viya Supervised Machine Learning Pipelines

Multiple-choice and short-answer questions

Official exam
Type
Written
Delivery
Both
Duration
90 min
Questions
53

Exam sections

01

SAS

SAS forms a distinct part of the capability assessed in SAS Viya Supervised Machine Learning Pipelines. The section brings together terminology, working methods, common constraints, and the judgment needed to deliver capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall SAS Viya Supervised Machine Learning Pipelines format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Translate the topic into three questions: what evidence is available, what action is justified, and what risk remains? Applying that structure to sas helps with both scenario questions and practical work.

02

Combining

Questions or tasks in this area explore combining from both conceptual and operational perspectives. Strong performance depends on connecting the topic to the broader responsibility of capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Candidates may encounter combining through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Turn the topic into a short teach-back exercise with a diagram, checklist, or command sequence. Revise it after hands-on practice so the final version reflects how combining behaves, not merely how it is described.

03

Programming

The programming area tests whether a candidate can move from recognition to correct action. It includes the reasoning, workflow awareness, and failure analysis needed when working with capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall SAS Viya Supervised Machine Learning Pipelines format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Rehearse the complete workflow for programming, including setup, validation, failure handling, and communication of the result. Keep notes on recurring mistakes and repeat the weakest step under time pressure. For the sas-data-scientist--a00-406 assessment, focus this exercise specifically on programming and the decisions a candidate must make in that context.

04

Capstone Combining Advanced SAS Programming With AI

Within SAS Viya Supervised Machine Learning Pipelines, capstone combining advanced sas programming with ai is treated as an applied capability rather than an isolated definition. Candidates should be ready to interpret context, identify an appropriate next step, and account for the operational goals behind capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Candidates may encounter capstone combining advanced sas programming with ai through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Collect several failure examples related to capstone combining advanced sas programming with ai and diagnose them from symptoms before looking at the solution. Prioritize repeatable investigation habits over memorizing a single successful path.

A00-407

Forecasting and Optimization Using SAS Viya

Multiple-choice and short-answer questions

Official exam
Type
Written
Delivery
Both
Duration
90 min
Questions
50

Exam sections

01

SAS

Forecasting and Optimization Using SAS Viya examines how candidates understand and apply sas within the wider credential scope. This area connects core concepts to the decisions, dependencies, and consequences practitioners encounter when carrying out the work described by capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall Forecasting and Optimization Using SAS Viya format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Build a small practice scenario around sas and complete it without relying on step-by-step prompts. Afterwards, explain why each decision was appropriate and identify the signal that would have changed your approach.

02

Combining

This area concentrates on combining as it appears in realistic tasks and scenarios. Candidates need to recognize the relevant inputs, choose a defensible approach, and understand how the result supports capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Candidates may encounter combining through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Practise explaining combining to a technical peer without reading definitions. Then validate the explanation by completing representative tasks and checking whether your result satisfies the intended objective.

03

Programming

Programming forms a distinct part of the capability assessed in Forecasting and Optimization Using SAS Viya. The section brings together terminology, working methods, common constraints, and the judgment needed to deliver capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall Forecasting and Optimization Using SAS Viya format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Review a realistic artifact connected to programming—such as a configuration, report, backlog, model, log set, or design—and identify both correct practice and subtle weaknesses that an assessment could probe. For the sas-data-scientist--a00-407 assessment, focus this exercise specifically on programming and the decisions a candidate must make in that context.

04

Capstone Combining Advanced SAS Programming With AI

Questions or tasks in this area explore capstone combining advanced sas programming with ai from both conceptual and operational perspectives. Strong performance depends on connecting the topic to the broader responsibility of capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Candidates may encounter capstone combining advanced sas programming with ai through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Use a lab, case study, or worked example to connect capstone combining advanced sas programming with ai to observable outcomes. Deliberately introduce one incorrect assumption, diagnose its effect, and document the correction in your own words.

A00-408

Natural Language Processing and Computer Vision

Multiple-choice and short-answer questions

Official exam
Type
Written
Delivery
Both
Duration
110 min
Questions
60

Exam sections

01

SAS

The sas area tests whether a candidate can move from recognition to correct action. It includes the reasoning, workflow awareness, and failure analysis needed when working with capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work. For the sas-data-scientist--a00-408 assessment, focus this exercise specifically on sas and the decisions a candidate must make in that context.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall Natural Language Processing and Computer Vision format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Work through one straightforward and one ambiguous example of sas. For the ambiguous case, state the assumptions you need, choose an approach, and describe how you would verify that choice. For the sas-data-scientist--a00-408 assessment, focus this exercise specifically on sas and the decisions a candidate must make in that context.

02

Combining

Within Natural Language Processing and Computer Vision, combining is treated as an applied capability rather than an isolated definition. Candidates should be ready to interpret context, identify an appropriate next step, and account for the operational goals behind capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Candidates may encounter combining through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Create a comparison sheet for the main options, commands, controls, or methods associated with combining. Test the distinctions against realistic cases so similar-looking choices do not become guesswork. For the sas-data-scientist--a00-408 assessment, focus this exercise specifically on combining and the decisions a candidate must make in that context.

03

Programming

Natural Language Processing and Computer Vision examines how candidates understand and apply programming within the wider credential scope. This area connects core concepts to the decisions, dependencies, and consequences practitioners encounter when carrying out the work described by capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work.

Question notes

Expect this topic to appear through scenario interpretation, objective questions, or practical tasks consistent with the overall Natural Language Processing and Computer Vision format. No separate question count or timing is assigned unless the provider publishes one.

Preparation tips

Map programming to the preceding and following stages of the real workflow. This exposes dependencies that isolated flashcards miss and makes it easier to reason through unfamiliar combinations on assessment day. For the sas-data-scientist--a00-408 assessment, focus this exercise specifically on programming and the decisions a candidate must make in that context.

04

Capstone Combining Advanced SAS Programming With AI

This area concentrates on capstone combining advanced sas programming with ai as it appears in realistic tasks and scenarios. Candidates need to recognize the relevant inputs, choose a defensible approach, and understand how the result supports capstone credential combining advanced SAS programming with professional AI and machine-learning capability for end-to-end data-science work. For the sas-data-scientist--a00-408 assessment, focus this exercise specifically on capstone combining advanced sas programming with ai and the decisions a candidate must make in that context.

Question notes

Candidates may encounter capstone combining advanced sas programming with ai through comparisons, troubleshooting prompts, configuration choices, analysis, or applied exercises. Exact distribution can change with the active exam form.

Preparation tips

Simulate the constraints of Natural Language Processing and Computer Vision while practising capstone combining advanced sas programming with ai. Limit references, capture evidence as you work, and reserve time to check completeness so technique and exam execution improve together.

Study effort

SAS Certified Data Scientist: Preparation strategy and expected study effort

Effective SAS Certified Data Scientist preparation moves from scope review to active practice. Learn the core concepts, apply them in realistic tasks, test recall and judgment, and reserve enough time to close gaps rather than cramming near the exam date.

Study time

440-760h

Difficulty

Recommended experience

30 months

Practice exam useful
Hands-on lab useful

Exam cost

SAS Certified Data Scientist: Exam price and the full cost of earning the certification

Use the structured fee rows for the latest known amount and compare region, tax, voucher, or membership notes before registering.

$180

United States

Standard priceTax may vary
United States$180
United States$180
United States$180

Prerequisites

What to know before starting SAS Certified Data Scientist

Candidates should have substantial experience with SAS programming, data preparation, statistics, predictive modeling, and analytic communication. Familiarity with SAS Viya and machine-learning workflows is valuable. Preparation should include full projects that begin with a problem and data, then progress through modeling, evaluation, explanation, and operational considerations.

Career fit

Roles and skills connected to this certification

Explore the roles and skills most directly connected to this certification, then use those paths to compare adjacent credentials.

RoleData Scientist

A data scientist frames analytical questions, prepares data, develops models, and communicates evidence that supports better decisions and products.

17 certificationsExplore
RoleStatistical Programmer

A statistical programmer uses code and statistical methods to prepare data, produce analysis outputs, validate results, and support evidence-based decisions.

14 certificationsExplore
RoleMachine Learning Engineer

Machine learning engineers are responsible for developing, deploying, and managing machine learning systems and their associated training workflows and model-serving pipelines in production environments.

26 certificationsExplore
RoleData Analyst

Data analysts interpret data, build analyses, and support decision-making through structured data exploration and insight generation.

47 certificationsExplore
SkillSAS Programming

SAS programming is the ability to use SAS code and procedures to prepare data, perform analysis, automate workflows, and produce controlled reporting outputs.

20 certificationsExplore
SkillSAS Viya

SAS Viya is the ability to use and administer SAS's cloud-enabled analytics platform for data preparation, modeling, deployment, governance, and collaboration.

20 certificationsExplore
SkillStatistical Modeling

Statistical modeling is the ability to represent relationships, uncertainty, and patterns in data with methods that support sound explanation, prediction, or decisions.

10 certificationsExplore
SkillMachine Learning Fundamentals

Understand the core principles of training, evaluating, and deploying machine learning models, forming the basis for many AI-driven applications.

17 certificationsExplore

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Related domains and industries

Use these subject and industry paths to understand where this credential fits inside the broader certification index.

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Plan your next credential move across the SAS certification catalog

Continue into individual SAS certifications to compare what each credential covers, how candidates are assessed, and which professional goals it may support. Check the complete credential details before choosing where to invest your preparation time.