The modern professional landscape is marked by intense competition and rapid technological advancement. The shift towards automation, artificial intelligence, and data-driven decision-making has created an ever-growing demand for individuals who possess both technical expertise and relevant certifications. Among the many opportunities for upskilling, earning the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 credential can be a decisive move for those aiming to establish themselves as experts in machine learning and advanced analytics.
This certification is not just another badge on a résumé. It represents a deep understanding of supervised machine learning pipelines using the SAS Viya 3.4 platform, which is recognized globally by employers seeking skilled data scientists.
Why SAS Certification Matters in Today’s Data-Driven World
As the need for machine learning experts continues to grow, so does the importance of standardized credentials that can objectively validate one’s skills. The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 exam was designed with this objective in mind, to assess whether a candidate can build, evaluate, and deploy machine learning models using SAS tools alongside open-source capabilities.
Professionals who are certified demonstrate that they are capable of contributing meaningfully to analytics projects. This certification aligns closely with real-world applications, making it valuable not only from a theoretical standpoint but also in practical business settings.
A Competitive Edge for Professionals
What truly sets certified professionals apart is not just their theoretical knowledge but their demonstrated ability to implement machine learning models that solve business problems. In companies where data science initiatives are central to strategic decision-making, professionals who hold this credential often become key contributors to analytics and innovation efforts.
Organizations that rely on SAS for their analytics infrastructure recognize this certification as a mark of capability and reliability. Holding this credential significantly increases the chances of career growth, job security, and higher compensation.
Who Should Consider This Certification?
This certification is ideally suited for professionals who are involved or interested in machine learning, data science, or statistical modeling. If your work involves building predictive models, analyzing large datasets, or working within a SAS-based analytics environment, then the SAS Certified Specialist credential is especially relevant.
The exam is structured in a way that accommodates not just SAS users but also those familiar with open-source tools like Python and R. Candidates who can integrate SAS tools with open-source workflows will find this certification particularly advantageous.
About the SAS Viya 3.4 Exam
The exam assesses your knowledge in key areas such as data sourcing, model building, and model assessment. It tests your hands-on proficiency with the Visual Data Mining and Machine Learning (VDMML) environment within SAS Viya. Candidates need to demonstrate how they can manage data through pipelines, apply various machine learning algorithms, and evaluate the performance of their models.
Passing the exam confirms that you have the practical skills to prepare data, select modeling techniques, and deploy models in a production environment using SAS Viya.
Skills Validated by the Certification
The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 credential validates the following key capabilities:
- Preparing and accessing data from various sources including cloud, local, and relational databases
- Using visual interfaces to create supervised machine learning models
- Selecting features, tuning hyperparameters, and validating model performance
- Comparing model outcomes and deploying the best-performing models
These skills are critical in business environments where timely and accurate predictions are essential.
Career Impact of the Certification
Professionals with this credential often see a marked improvement in job prospects. SAS is a leader in analytics and is used by more than 80,000 organizations worldwide. Companies that already use SAS for their data needs look favorably upon individuals who can hit the ground running with practical knowledge of SAS Viya.
Because the exam incorporates aspects of supervised learning using graphical interfaces, it is accessible to professionals who may not have deep programming experience but understand analytical workflows and model development. This opens the door for business analysts and statisticians to transition into machine learning roles.
Certification Structure and Domains
The exam is divided into three main sections:
- Data Sources (30%)
- Building Models (50%)
- Model Assessment and Deployment (20%)
Understanding the exam breakdown helps candidates focus their preparation and allocate time effectively to different learning areas.
Why SAS Viya 3.4 Is a Strong Platform
SAS Viya 3.4 is a powerful platform that brings together SAS’s robust analytics capabilities with modern features like in-memory processing, open-source integration, and scalability across cloud and on-premises environments. What makes SAS Viya especially effective for machine learning is its user-friendly interface, which allows drag-and-drop functionality for building machine learning workflows, as well as support for Python and R code integration.
Familiarity with the SAS Viya interface and its Visual Data Mining and Machine Learning environment is essential. The exam is performance-based, so practical experience within the platform will significantly improve your chances of success.
Real-World Application of SAS Certified Skills
The knowledge gained from preparing for this certification is not limited to passing the exam. These are real-world skills that can be applied to industry scenarios like fraud detection, customer churn analysis, supply chain optimization, and more.
Companies that rely on data-driven insights will often use SAS Viya to manage large-scale analytics workflows. Certified professionals become valuable contributors in cross-functional teams that bridge the gap between data engineering, business analysis, and strategic planning.
The demand for skilled professionals in machine learning and analytics is at an all-time high. By earning the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 credential, you’re doing more than adding a line to your résumé — you’re proving that you have what it takes to extract actionable insights from complex data using a world-class analytics platform.
This series designed to give you all the resources, strategies, and motivation you need to succeed. In the next part, we will dive into the core exam objectives, breaking down each domain, and providing detailed strategies for how to approach your preparation.
Understanding the SAS Viya 3.4 Exam Structure
When preparing for the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 exam, it’s essential to understand how the exam is structured. The exam is performance-based and tests practical abilities using real-world scenarios within the SAS Viya platform. It doesn’t only check theoretical knowledge but also challenges candidates to apply their understanding by building supervised machine learning models through SAS pipelines.
The exam covers three core domains: data sources, model building, and model assessment and deployment. Each domain has specific topics and skills that candidates must master to pass the exam successfully. Familiarity with the Visual Data Mining and Machine Learning (VDMML) interface in SAS Viya is critical, as this is the main environment used in the exam.
Domain 1: Data Sources (30%)
This section of the exam focuses on the candidate’s ability to connect, prepare, and manage data. A strong understanding of data access and transformation techniques is required. Candidates need to work with data from multiple sources including local files, cloud storage, and relational databases.
Key skills tested include:
- Importing data into SAS Viya
- Exploring and profiling data
- Performing basic data transformations
- Handling missing values and data types
- Creating pipelines for data input
The ability to prepare clean, structured data is foundational for any machine learning task. Candidates must know how to use the SAS Viya visual interface to create data flows that can be reused and easily integrated into machine learning pipelines.
Domain 2: Building Models (50%)
This is the most heavily weighted section of the exam and focuses on building and tuning machine learning models. Candidates are expected to use a range of algorithms and methods within the SAS Viya environment. The emphasis is on creating pipelines, selecting relevant features, and applying suitable modeling techniques.
Important skills in this section include:
- Applying classification and regression models
- Using decision trees, random forests, gradient boosting, and support vector machines
- Creating neural networks within the Viya platform
- Performing feature engineering and selection
- Fine-tuning hyperparameters using the Model Studio interface
- Comparing models within the same pipeline
SAS Viya offers tools that make it easy to compare multiple models simultaneously. Knowing how to interpret lift charts, ROC curves, and fit statistics is critical in selecting the best model for deployment.
Domain 3: Model Assessment and Deployment (20%)
The final domain of the exam evaluates whether the candidate can assess model performance and deploy the chosen model effectively. A good model isn’t just accurate—it must also be stable, interpretable, and deployable into real-world systems.
Skills required in this domain include:
- Assessing model performance using accuracy, precision, recall, and AUC
- Detecting overfitting and underfitting
- Creating score code for deployment
- Exporting models for use in other environments
- Automating scoring through pipelines
This section of the exam mirrors actual business practices where models must be put into production, not just developed in isolated environments. Candidates need to show they can complete the full lifecycle of a machine learning model—from ingestion to deployment.
Preparation Strategy for SAS Viya 3.4 Certification
Studying for this certification requires a structured and hands-on approach. The exam is based on performance, so practical skills within the Viya platform are crucial. Here are the recommended steps to build a successful preparation strategy.
Familiarize Yourself with the SAS Viya Interface
Spend time navigating the Visual Data Mining and Machine Learning interface. Understanding how to create a new pipeline, access data, and drag-and-drop model nodes is essential. Practice opening existing projects, modifying them, and reviewing the results using the model comparison tools.
Candidates are advised to practice building pipelines multiple times with different datasets to get comfortable with the environment. This repetition will help reduce time spent navigating the interface during the actual exam.
Follow the Official Learning Path
SAS offers an official learning path for this certification that includes training videos, hands-on labs, and sample projects. These resources are tailored to cover all topics included in the certification domains and mirror the format of the actual exam.
Following the learning path ensures that no topic is missed and gives candidates access to curated exercises that build real competence. These are particularly useful for individuals new to SAS Viya or transitioning from other tools like Python or R.
Use Sample Questions and Practice Exercises
Taking practice exams and working on sample questions can help identify strengths and weaknesses. Candidates should analyze their performance in each domain and target the areas where they score lowest.
Practice exercises also help simulate exam conditions, which can reduce anxiety and improve timing. Candidates should focus on understanding the reasoning behind each question, not just memorizing answers.
Join Online Study Communities
Study groups and online communities offer support, feedback, and additional learning materials. Participating in discussion forums can expose candidates to different perspectives, problem-solving approaches, and preparation techniques.
Being part of a community can also help you stay motivated and learn from others who have already passed the exam. It provides a space to ask technical questions and share resources, which is especially helpful for complex topics.
Review the Official Documentation
The official SAS documentation is an invaluable resource for mastering the Viya environment. It contains detailed technical information, parameter explanations, and advanced use cases. Reviewing the documentation deepens your understanding and prepares you for unexpected questions in the exam.
Documentation is also a good source for learning about new features, command syntax, and configurations that may not be covered in training videos or online courses.
Focus on Hands-On Practice
The exam tests practical skills, so hands-on practice is not optional—it is mandatory. Candidates should allocate time to build pipelines from scratch, train multiple models, evaluate outputs, and deploy results.
Creating personal projects using publicly available datasets can make preparation more engaging. These self-driven exercises help reinforce learning and simulate the tasks you’ll be asked to perform in the actual test.
Allocate Study Time Based on Exam Weighting
Since the Building Models section holds 50% of the exam’s weight, candidates should devote half of their preparation time to mastering that domain. Prioritize tasks like experimenting with different model types, hyperparameter tuning, and performance comparison.
While the other domains are equally important, their lower weighting means they can be addressed more efficiently. A balanced schedule that mirrors the exam structure will lead to a more effective preparation plan.
Avoid Common Pitfalls During Preparation
Many candidates underestimate the importance of the data sourcing and deployment sections. These are often viewed as secondary skills, but they play a crucial role in model success. Skipping these areas may result in unexpected challenges during the exam.
Another mistake is relying too heavily on theory. Reading about machine learning concepts without applying them through the SAS interface won’t prepare you for a performance-based test. Focus on executing tasks, interpreting results, and solving problems inside the actual software environment.
Stay Updated with Platform Changes
Although this certification focuses on version 3.4 of SAS Viya, it’s important to stay current with the platform’s capabilities. Minor updates may introduce new interface changes or features that appear in the exam environment. Reviewing the latest release notes on the SAS website can keep you informed.
This proactive approach ensures you’re not surprised by new functionalities or layout shifts during your practice or on exam day.
The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 certification is a rigorous but rewarding goal for professionals seeking to strengthen their machine learning expertise. The exam not only validates your technical skills but also ensures you’re ready to apply them in practical business environments.
Preparation must be comprehensive and hands-on, with a strong emphasis on using the SAS Viya interface to manage real datasets and build efficient models. By understanding the structure and content of the exam, aligning your study strategy to the domain weights, and consistently practicing, you can approach the test with confidence.
In the article, we will explore the complete range of learning resources available—from training courses and documentation to books, study groups, and practice tests—that can support your journey toward certification.
Introduction to Study Resources
Preparing for the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 exam requires more than just a casual review of course materials. To succeed in this performance-based certification, candidates need to adopt a well-structured study plan supported by a variety of learning resources. Whether you’re a beginner to SAS Viya or someone looking to formalize existing knowledge with a credential, having the right study tools can make all the difference.
This guide focuses on essential study resources available for candidates preparing for the certification. These resources are categorized for easier navigation—official materials, practical exercises, books, and community learning are all covered.
Start with the Official SAS Study Guide
The official SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 study guide is the most reliable starting point for preparation. It’s designed by SAS experts who understand the structure and expectations of the exam. This guide provides a comprehensive breakdown of each domain, including key concepts, terminology, and sample tasks.
Working through this official guide helps you build a strong foundation in using the Visual Data Mining and Machine Learning environment in SAS Viya. It also includes use cases and example workflows that simulate what you’ll encounter in the exam.
Explore the SAS Viya Documentation
SAS documentation is often overlooked, but it is one of the richest resources for technical preparation. The SAS Viya documentation provides detailed information on the tools and features you’ll use during the exam. You’ll find guidance on how to use different modeling nodes, configure parameters, interpret outputs, and deploy models.
It’s especially useful for understanding how different machine learning techniques are implemented in Viya. For example, if you’re working with gradient boosting or neural networks, the documentation will show you how to control aspects like learning rate, tree depth, and training cycles.
Register for SAS Training Courses
SAS offers instructor-led and on-demand training programs specifically tailored for certification. These courses are designed to mirror the structure of the exam and include hands-on labs within a virtual SAS Viya environment. Topics are aligned with each exam domain and cover practical applications such as building supervised learning models, comparing models, and deploying scoring pipelines.
One of the most recommended courses is Machine Learning Using SAS Viya, which includes step-by-step tutorials, datasets for practice, and exercises that reinforce learning through real-world scenarios. These sessions are ideal for professionals who prefer structured learning with instructor support.
Leverage the SAS Learning Subscription
The SAS Learning Subscription is a valuable resource for long-term learners. It gives you access to all courses in the Advanced Analytics learning path, including those focused on machine learning and Viya. This subscription model is ideal for individuals who want to explore beyond certification and build a comprehensive skill set across the SAS ecosystem.
It allows learners to study at their own pace and revisit topics as needed. The courses include interactive videos, labs, quizzes, and downloadable resources, making them suitable for deep and flexible learning.
Practice with Real-World Datasets
Using real-world datasets for practice is essential for hands-on preparation. Publicly available datasets such as those from Kaggle, UCI Machine Learning Repository, and open government databases offer a wide variety of data types and modeling challenges.
Import these datasets into SAS Viya and build complete machine learning pipelines. Practice cleaning, transforming, and modeling the data using different algorithms and compare their results. This process mimics the exam environment and builds confidence in handling unseen data during the test.
Create Your Own Practice Projects
Self-initiated projects are one of the best ways to reinforce learning. Choose a business problem such as credit risk modeling, customer churn prediction, or sales forecasting, and build an end-to-end pipeline using SAS Viya. Include tasks like feature selection, model comparison, and deployment strategy.
Creating your own projects gives you full control over the workflow and helps develop a problem-solving mindset. It allows you to apply multiple models to the same dataset, compare their performance, and gain insights that theory alone cannot offer.
Use Flashcards for Quick Recall
Flashcards are a great way to reinforce important concepts and terminology. These tools are particularly effective for reviewing formulas, metrics, modeling steps, and evaluation criteria. Create digital or physical flashcards based on key terms found in the study guide and documentation.
Some candidates use spaced repetition systems to improve retention of complex terms such as AUC, ROC, bias-variance trade-off, and model overfitting. Reviewing flashcards daily enhances memory and ensures that critical concepts are readily accessible during the exam.
Join Study Groups and Online Communities
Collaborating with others preparing for the same certification can improve your understanding and keep you motivated. Study groups offer the benefit of group discussions, resource sharing, and peer reviews. These groups often share tips for navigating the SAS Viya interface, troubleshooting errors, and understanding difficult topics.
Participating in online forums allows you to ask questions, post solutions, and learn from candidates who have already passed the exam. Communities such as Reddit, LinkedIn, and the SAS Support Communities are excellent platforms to connect with peers and experts alike.
Practice Tests: Simulating the Real Exam
One of the most effective ways to prepare is to simulate the real test environment using practice exams. These mock exams help identify your strong and weak areas. They also build familiarity with the types of tasks and questions you’ll encounter in the certification test.
Practice tests often include case-based questions that require building and interpreting models in a controlled environment. Treat these mock exams as real assessments and time yourself. This improves your pacing and decision-making under pressure, which is crucial during the actual exam.
Refer to SAS Learning Books
In addition to the official guide, several SAS learning books can aid your preparation. These books offer comprehensive tutorials, best practices, and advanced tips for building machine learning models. Titles focused on predictive modeling, supervised learning, and SAS Viya workflows are especially useful.
Books provide in-depth explanations and contextual understanding that go beyond interface navigation. They’re great for candidates who want to deepen their understanding or are transitioning from other analytics platforms.
Hands-On Labs and Virtual Environments
Practicing in a live SAS Viya environment is critical. If you’re not working in an organization that already uses SAS Viya, you can access virtual labs through SAS training subscriptions or learning portals. These labs provide a cloud-based platform to experiment with pipelines, test models, and navigate the VDMML interface.
Time spent in the lab helps build muscle memory for model building and scoring procedures. Familiarity with the interface reduces errors and saves time during the certification exam.
Set a Personalized Study Schedule
Consistency is key to exam success. Based on your strengths and availability, create a customized study plan that spans at least 4 to 6 weeks. Allocate specific days to each exam domain and alternate between theory, hands-on practice, and revision.
A typical weekly schedule could include two days for watching video tutorials, two days for practicing in the Viya interface, and one day for revision or taking a practice test. Regular study sessions ensure steady progress and reduce exam anxiety.
Seek Feedback from Peers or Mentors
If you’re part of a study group or have access to experienced SAS professionals, ask them to review your pipelines, explain results, or suggest improvements. Feedback helps correct mistakes early and sharpens your decision-making.
If possible, find a mentor who’s already certified. Their insights can save you time and guide you toward the most efficient preparation strategies. Learning from someone who’s been through the process adds both technical value and encouragement.
Stay Updated on Exam Details
Certification exams evolve with software updates and industry practices. Even though the focus is on SAS Viya 3.4, stay updated with the latest announcements on the SAS website regarding changes in exam format or software environment.
Being aware of updates ensures your preparation aligns with what you’ll face on exam day. It also positions you to stay current with SAS Viya developments beyond the exam.
The path to earning the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 credential is rigorous but highly achievable with the right study resources. From official documentation and structured training to self-driven projects and community support, every tool plays a role in reinforcing your skills.
Combining theory with hands-on practice is the key to mastering the concepts and passing the performance-based exam. Organize your resources, plan your study time, and practice regularly to build confidence in your ability to perform every task the exam demands.
Preparing for the Final Stretch
As the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 exam approaches, preparation needs to transition from learning mode to execution mode. You’ve spent time reviewing key concepts, building models, and simulating real-world tasks in SAS Viya. Now, it’s time to focus on polishing your workflow, managing time efficiently, and entering the exam environment with confidence.
This guide focuses on pre-exam strategies, test-day execution, and the steps you can take post-certification to maximize the impact of your achievement in the analytics and data science world.
Solidify Your Hands-On Skills
With a performance-based exam like this, it’s important to enter the test knowing not only what each model does but also how to build it correctly in the interface. You should be comfortable creating full machine learning pipelines, transforming data, selecting features, training and comparing models, and preparing the best-performing model for deployment.
Practice scenarios that include the entire workflow from importing datasets to assessing model fit and generating scoring code. This end-to-end approach helps ensure that you don’t stall on simple steps during the actual exam, where time management will be essential.
Review Model Performance Metrics
The certification exam requires more than just the ability to create models—you must also interpret their performance correctly. Spend time revisiting evaluation metrics used in SAS Viya such as:
- Area Under the ROC Curve (AUC)
- Lift and gains charts
- Misclassification rate
- Precision, recall, and F1-score
- Confusion matrices
Understand what each metric represents and how it impacts business decisions. During the exam, you may be asked to compare models using multiple metrics, so clarity here is crucial.
Revisit Frequently Used Tools in Viya
Within the Visual Data Mining and Machine Learning interface, certain tools appear regularly. Make sure you are confident in using nodes like:
- Data Partition
- Imputation
- Variable Selection
- Decision Tree
- Gradient Boosting
- Support Vector Machine
- Model Comparison
- Score Node
Knowing where to find these nodes, how to configure them, and how to interpret their outputs is a practical necessity. Also, remember that many tasks have multiple ways of being performed in Viya—choose the method that’s fastest and most reliable for you.
Familiarize Yourself with Exam Logistics
Before exam day, ensure that all logistics are confirmed. If the exam is proctored online, check your system requirements, internet connection, and ID verification procedures. If you’re taking the test at a center, know the location, arrival time, and necessary materials.
You will be provided access to a simulated SAS Viya environment during the exam. You won’t have access to external tools, so practice operating independently within the environment. Time yourself while working through mock tasks to improve speed and accuracy.
Night Before the Exam
Avoid last-minute cramming. Focus on reviewing your summary notes, visual pipelines, and key model comparisons. Rest is just as important as preparation. A calm, clear mind will help you think logically and avoid careless mistakes during the test.
Prepare your identification documents, test login details, and a clean test environment if you’re taking the exam remotely. Make sure your workspace meets the proctoring requirements to avoid any interruptions.
Exam Day Strategy
Begin your exam session with confidence. You will typically be given a series of hands-on tasks or scenarios that align with the three major exam domains: data sources, model building, and deployment. Follow these exam-day strategies:
- Read instructions carefully before starting each task
- Complete tasks in the order provided unless you’re more confident starting elsewhere
- Allocate time based on task complexity; don’t get stuck on a single item
- Save your progress regularly if the exam system allows
- Double-check outputs before submitting each task
Stay composed throughout the exam. If a pipeline isn’t producing expected results, retrace your steps and verify data partitioning, modeling inputs, or scoring settings. Logical troubleshooting is more important than memorizing every option.
Post-Exam Steps
After completing the exam, results are typically provided quickly, depending on the exam format. If you pass, congratulations—you’ve earned a credential that validates your ability to build, evaluate, and deploy machine learning models using SAS Viya.
You’ll receive an official certificate and a digital badge. These credentials can be added to your professional profiles, including LinkedIn and your resume, to highlight your technical skills and industry readiness.
Share Your Certification
Let your network know about your achievement. Sharing your SAS certification on LinkedIn or other professional platforms increases visibility and demonstrates commitment to data science proficiency.
You can also use the digital badge issued by SAS to showcase verified skills. Many hiring managers and recruiters look for credible certifications that match real-world responsibilities—this credential fits that requirement well.
Career Advantages of SAS Viya Certification
Becoming a SAS Certified Specialist can open new opportunities in roles like:
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Analyst
- Predictive Modeler
- Risk Analyst
Employers value candidates who can not only design algorithms but also put models into production. The ability to build scalable, interpretable, and operational models using SAS Viya is a highly marketable skill, especially in industries like banking, healthcare, and retail where compliance and performance matter.
Positioning Yourself in the Job Market
Once certified, it’s important to back up your credential with practical stories or examples. If you’ve worked on machine learning projects—even during training—highlight those experiences in interviews or portfolios. Discuss your workflow from data ingestion to deployment and the reasoning behind model selection.
Additionally, showcase your understanding of business implications. Hiring managers are increasingly looking for professionals who can connect technical insights to strategic decisions. Use your SAS Viya certification as a springboard to demonstrate that connection.
Continue Learning Beyond the Certification
The world of machine learning is evolving, and continuous learning is essential. Once certified, you can expand your expertise by exploring additional SAS certifications in advanced analytics, artificial intelligence, or forecasting. These complementary credentials strengthen your profile and help you stay current with industry standards.
Consider learning integrations between SAS and open-source tools like Python or R. SAS Viya supports code-based extensions, which can make you a more versatile analyst.
Engage in the SAS Community
Certification opens doors to a global community of SAS professionals. Engage with them through SAS forums, webinars, and user groups. These networks offer valuable insight into industry applications, career growth, and emerging trends in machine learning.
You can also contribute back by sharing your certification journey, helping others prepare for their exams, or presenting your own use cases and projects in community discussions.
Set Your Next Goal
Professional development is an ongoing process. Now that you’ve achieved the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 credential, define your next objective. That could be mastering deep learning techniques, exploring AI ethics, or leading analytics projects in your organization.
Certifications are milestones—but they’re also tools for transformation. Use what you’ve learned not just to advance your own career but to bring innovation and value to your team, clients, or business stakeholders.
Final Thoughts
Earning the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 certification is a significant professional achievement. It proves that you can operate effectively in a high-powered analytics environment, build accurate and scalable models, and apply data science techniques to solve real-world problems.
The journey involves focused study, consistent practice, and a strong understanding of machine learning concepts and SAS Viya’s capabilities. But once completed, it places you among a group of professionals equipped to drive analytics transformation in their fields.
Your next step is to use this knowledge to grow, lead, and innovate. Whether you’re stepping into a new role, aiming for a promotion, or building analytics solutions for complex challenges, this certification sets you apart and positions you for success.