Whether you’re a seasoned analyst or a tech-savvy individual hungry for new challenges, this blog serves as your comprehensive guide to conquering the SAS Certified Specialist exam. We’ll equip you with essential strategies, valuable resources, and expert tips to confidently navigate the assessment and demonstrate your mastery of machine learning with SAS Viya 4.0. So, get ready to start on a journey that will elevate your career in the data science landscape.

Machine Learning Using SAS Viya 4.0 Exam Overview

Before diving into preparation, let’s familiarize ourselves with the exam itself. The SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 certification validates your ability to build and deploy supervised machine learning models using SAS Viya. It’s ideal for data scientists and analysts who want to demonstrate their expertise in this specific area.

• The exam is jointly administered by SAS and Pearson VUE and consists of 50-55 multiple-choice and short-answer questions.
• Candidates are allotted 90 minutes to complete the exam, with a passing score set at 62%.
• Certification earned from this exam is valid for 5 years, and it is centered around SAS Viya 4.0.
• The exam fee in the US and most other countries is $180.

Prerequisites:

• While no formal prerequisites exist, prior experience with data analysis and a basic understanding of machine learning concepts are highly recommended.
• Familiarity with the SAS Viya platform and its functionalities is essential for success. Consider taking introductory courses or utilizing practice environments beforehand.

Preparation Strategies for Machine Learning Using SAS Viya 4.0 Exam

Having navigated the exam overview, let’s now explore the preparation strategies. To guide you toward success, below are some of the best methods essential for your preparation:

1. Understand the Exam Objectives

Thoroughly going through the course outline is crucial to getting ready for the exam, and making sure you cover everything you need to. Reviewing the exam objectives multiple times not only helps you understand the concepts faster but also boosts your confidence. Regularly revising keeps the information fresh in your mind, increasing your chances of doing well on the exam day. The objectives include:

Understand Data Sources (30 – 36%)

Creating a project in Model Studio

• Bringing data into Model Studio for analysis
  • Importing data from a local source (Import tab)
  • Adding data from a stored data source (Data Sources tab)
  • Using an in-memory data source (Available tab)
• Creating Model Studio Pipelines with the New Pipeline window
  • Automatically generate pipelines
  • Pipeline templates
• Advanced Advisor options
  • Maximum class level
  • Maximum % missing
  • Interval cut-off
• Partition data into training, validation, and test
  • Explaining why partitioning is important
  • Understanding the different methods to partition data (stratified vs simple random)
• Using Event Based Sampling for rare events.
• Setting up Node Configuration

Exploring the data

• Using the DATA EXPLORATION node
• Profiling data during data definition
• Preliminary data exploration using the data tab
• Saving data with the SAVE DATA node

Modifying data

• Explaining concepts of replacement, transformation, imputation, filtering, outlier detection
• Modifying metadata within the DATA tab
• Modifying metadata with the MANAGE VARIABLES node
• Using the REPLACEMENT node to update variable values
• Utilizing the TRANSFORMATION node to correct problems with input data sources, such as variables
  distribution or outliers
• Using the IMPUTE node to impute missing values and create missing value indicators
• Preparing text data for modeling with the TEXT MINING node
• Explaining common data challenges and remedies for supervised learning

Utilizing the VARIABLE SELECTION node to identify important variables to be included in a predictive model

• Unsupervised Selection
• Fast Supervised Selection
• Linear Regression Selection
• Decision Tree Selection
• Forest Selection
• Gradient Boosting Selection
• Create Validation from Training
• Use multiple methods within the same VARIABLE SELECTION node

Learn about Building Models (40 – 46%)

Describe key machine learning terms and concepts

• Data partitioning: training, validation, test data sets
• Observations (cases), independent (input) variables/features, dependent (target) variables
• Measurement scales: Interval, ordinal, nominal (categorical), binary variables
• Supervised vs unsupervised learning
• Prediction types: decisions, rankings, estimates
• Curse of dimensionality, redundancy, irrelevancy
• Decision trees, neural networks, regression models, support vector machines (SVM)
• Model optimization, overfitting, underfitting, model selection
• Describe ensemble models
• Explain autotuning

Building models with decision trees and ensemble of trees

• Explaining how decision trees identify split points
  • Split search algorithm
  • Recursive partitioning
  • Decision tree algorithms
  • Multiway vs. binary splits
  • Impurity reduction
  • Gini, entropy, Bonferroni, IGR, FTEST, variance, chi-square, CHAID
  • Compare methods to grow decision trees for categorical vs continuous response variables
• Explaining the effect of missing values on decision trees
• Explaining surrogate rules
• Understanding the purpose of pruning decision trees
• Explaining bagging vs. boosting methods
• Build models with the DECISION TREE node
  • Adjust splitting options
  • Adjust pruning options
• Creating models with the GRADIENT BOOSTING node
  • Adjust general options: number of trees, learning rate, L1/L2 regularization
  • Adjust Tree Splitting options
  • Adjust early stopping
• Build models with the FOREST node
  • Adjust number of trees
  • Adjust tree splitting options
• Interpret decision tree, gradient boosting, and forest results (fit statistics, output, tree diagrams, tree maps, variable importance, error plots, autotuned results)

Building models with neural networks

• Describing the characteristics of neural network models
  • Universal approximation
  • Neurons, hidden layers, perceptrons, multilayer perceptrons
  • Weights and bias
  • Activation functions
  • Optimization Methods (LBFGS and Stochastic Gradient Descent)
  • Variable standardization
  • Learning rate, annealing rate, L1/L2 regularization
• Build models with the NEURAL NETWORK node
  • Adjust number of layers and neurons
  • Adjust optimization options and early stopping criterion
• Interpret NEURAL NETWORK node results (network diagram, iteration plots, and output)

Build models with support vector machines

• Describing the characteristics of support vector machines.
• Build a model with the SVM node
  • Adjust general properties (Kernel, Penalty, Tolerance)
• Interpret SVM node results (Output)

Using Model Interpretability tools to explain black box models

• Partial Dependence plots
• Individual Conditional Expectation plots
• Local Interpretable Model-Agnostic Explanations plots
• Kernel-SHAP plots

Incorporate externally written code

• Open Source Code node
• SAS Code node
• Score Code Import node

Understand Model Assessment and Deployment Models (24 – 30%)

Explaining the principles of Model Assessment

• Explaining different dimensions for model comparison
  • Training speed
  • Model application speed
  • Tolerance
  • Model clarity
• Explaining honest assessment
  • Evaluating a model with a holdout data set
• Using the appropriate fit statistic for different prediction types
  • Average error for estimates
  • Misclassification for decisions
• Explaining results from the INSIGHTS tab

Assessing and comparing models in Model Studio

• Comparing models with the MODEL COMPARISON node
• Comparing models with the PIPELINE COMPARISON tab
• Interpreting Fit Statistics, Lift Reports, ROC reports, Event Classification chart
• Interpreting Fairness and Bias plots

Deploying a model

• Exporting score code
• Registering a model
• Publish a model
• SCORE DATA node

2. Use the SAS Exam Training Course

Machine Learning Using SAS Viya

This 14-hour course covers the basic theories behind supervised machine learning models. It uses practical demonstrations and exercises to help understand these concepts and how they can be used to solve business problems. Additionally, it includes a case study to guide participants through all stages of solving real-world problems using data analysis, from understanding the problem to deploying the model. This course is a key part of the SAS Viya Data Mining and Machine Learning curriculum. It focuses on Model Studio, a tool in SAS Viya for preparing, developing, comparing, and deploying advanced analytics models. You’ll learn how to train supervised machine learning models to make better decisions with big data.

In this course, you will learn how to:

• Implement the analytical life cycle to business needs.
• Solve business problems using analytical approaches.
• Explore data for building analytical models.
• Find the best features for predictive modeling.
• Create different types of supervised learning models, like decision trees, tree ensembles, neural networks, and support vector machines.
• Select the best model based on business requirements.
• Manage analytical models for production.

This course is suitable for business analysts, data analysts, marketing professionals, data scientists, and others working in related fields. Before taking this course, participants should have a basic understanding of statistics and machine learning concepts. Previous experience with SAS software is helpful but not necessary.

3. Use Reference Books

SAS Institute offers a helpful resource to help in your exam preparation: the first edition of the “Machine Learning with SAS Viya” book. This book provides detailed guidance on utilizing SAS Model Manager tools alongside open-source platforms. It highlights the features of SAS Model Studio to demonstrate machine learning processes within SAS Viya. The book also includes demonstrations, practice exercises, and quizzes to enhance your proficiency.

Within this book, you will explore:

• Supervised and unsupervised machine learning techniques.
• Strategies for preparing data and handling missing or unstructured data.
• Building and selecting models suited to your needs.
• Techniques for refining and optimizing models.
• Deployment of models and monitoring their performance over time.

4. Use Free SAS Certification Webinars

In the webinar, the specialists discuss the latest updates in the SAS certification offerings, showcasing how SAS has contributed to the career progression of analytics professionals and providing advice for initiating your certification journey. During the webinar, you’ll discover:

• The benefits a SAS certification brings to your organization.
• Strategies for persuading management about the significance of certification.
• The procedures SAS implements to safeguard certification authenticity and credibility.
• Information about newly introduced SAS certifications.
• Resources and support from SAS to aid in your exam preparation.

5. Take Practice Tests

Engaging in practice tests is an excellent strategy to enhance your preparation for exams. These tests simulate the exam environment and help you become familiar with the types of questions you may encounter. Additionally, they enable you to identify areas where you need more focus and gauge your readiness for the actual exam. Therefore, incorporating practice tests into your study routine can significantly improve your confidence and performance on the day of the exam.

FAQs: SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 Exam

Below are some of the frequently asked questions for the SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 Exam:

Is SAS good for machine learning?

Yes, SAS can be good for machine learning, but it depends on your specific needs and priorities. Here’s a quick overview:

Pros:

• User-friendly interface: SAS is known for its graphical user interface and point-and-click functionality, making it accessible to users with less coding experience.
• Comprehensive tools: SAS offers a wide range of machine learning algorithms and tools for data preparation, model building, evaluation, and deployment.
• Integration with existing SAS infrastructure: If you already use SAS for other analytics tasks, integrating machine learning workflows can be streamlined.
• Strong support and community: SAS provides extensive documentation, training, and a supportive user community.

Cons:

• Cost: SAS licensing can be expensive compared to some open-source alternatives.
• Flexibility: SAS may not be as flexible as some Python libraries for building custom models or exploring cutting-edge algorithms.
• Learning curve: While user-friendly, mastering SAS still requires some investment in learning its interface and functionalities.

Which companies use SAS Viya?

Many companies across various industries utilize SAS Viya, including:

• Fortune 100 companies: Over 90% of them are SAS customers, indicating widespread adoption.
• Financial institutions: JP Morgan Chase, Standard Bank Group, IDBI Bank, etc.
• Telecommunications: Siemens, Lockheed Martin, etc.
• Retail and consumer goods: Office Depot, Migros Money, etc.
• Utilities: The Southern Company, etc.
• Healthcare: iGA Istanbul Airport, etc.
• Life sciences: READDI, etc.

What can you do with a SAS certificate?

Earning a SAS certification can open doors to various opportunities depending on the specific certificate you obtain. Here are some general benefits:

• Demonstrate your proficiency in using SAS for data analysis, reporting, and other tasks.
• Stand out from other candidates when applying for data analyst, data scientist, or related roles.
• Increase your earning potential, as certified professionals may command higher salaries.
• Earning a recognized certification validates your skills and knowledge in the eyes of employers and peers.
• Gain access to the SAS Global Certified Professional Directory, showcasing your credentials to potential employers.
• Boost your confidence and expertise in using SAS for various data-driven tasks.

Conclusion

Remember, the path to becoming a SAS Certified Specialist in Machine Learning is not just about passing an exam, it’s about unlocking your potential in this dynamic field. Embrace the learning process, utilize the resources provided, and believe in your ability to succeed. This certification serves as a stepping stone, opening doors to exciting opportunities and empowering you to contribute meaningfully to the ever-evolving world of data science. So, begin on this journey with confidence, and remember, the SAS community is here to support you every step of the way.

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The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 certification validates that you possess the knowledge of Visual Data Mining and Machine Learning software. Furthermore, the skills involved in this certification include feature engineering and preparing data. Moreover, the step by step structure of creating supervised machine learning models. Then assessing model performance and deploying models into production.

Why get SAS Certified?

First and foremost, SAS is a statistical software package created by the SAS Institute. This program also has data management, sophisticated analytics, and multivariate analysis capabilities. SAS is used in corporate intelligence, criminal investigations, and predictive analytics as a result. The SAS certification has a high value because of the software’s benefits.

This certification is excellent for landing a business analyst job. It demonstrates your expertise in SAS programming. This certification is very hands-on, meaning it’s practical and tailored to job needs. It covers various subjects like querying databases, data analysis, importing and exporting data files, data editing, merging data sets, and generating reports.

Who should take the exam?

The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 certification is for data scientists. For those who create supervised machine learning models using pipelines in SAS Viya. The certificate ensures that candidates showcase their talents. The talents of AI and Analytics using Open source and SAS tools. All this is to acquire insights from data. This certification targets the following individuals –

• Firstly, Business analysts 
• Secondly, Data analysts 
• Marketing analysts
• Also, Marketing managers
• Then, Data engineers
• Financial analysts
• Finally, Data miners

Knowledge and prerequisite:

To appear for the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 certification exam you need to have the knowledge of Basic Statistics and Online course by SAS for Machine Learning.

With this, you’ve familiarized yourself with the chief purpose, audience, and objectives of the exam. It is now time to dig deeper and gain a better understanding of the exam. Gaining in-depth knowledge of the exam will help you prepare the best suitable preparation strategy to pass the exam in the very first attempt.

Exam Format:

The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 exam comes with an ID (A00-401) and is administered by SAS and Pearson VUE. This exam is based on SAS Viya 3.4. Further, it has 50-55 multiple-choice and short-answer questions and the candidate should obtain a minimum of 70% to pass the exam. Finally, the candidates will be given 90 minutes to complete the Machine Learning exam.

Let us now turn our attention to the more technical aspect of your preparation, namely the course outline. Going over the many domains included in the exam thoroughly will assist you in identifying the areas where your abilities will be put to the test. You’ll also learn which areas you already know a lot about and which ones you need to focus on.

Exam Course Outline

The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 certification exam covers the following domains –

Domain 1: Data Sources (30%)

• Firstly, create a project in Model Studio
• Explore the data
• Also, modify data
• Reduce the dimensionality of the data
• Finally, use the VARIABLE SELECTION node to identify important variables

Domain 2: Building Models (50%)

• Firstly, describe key supervised machine learning terms and concepts
• Build models with decision trees and ensemble of trees
• Also, build models with neural networks
• Build models with support vector machines
• Finally, incorporate externally written code

Domain 3: Model Assessment and Deployment (20%)

• Firstly, explain the principles of Model Assessment
• Assess and compare models in Model Studio
• Finally, deploy a model

Preparation Guide for the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 Exam

Becoming a SAS Certified Specialist in Machine Learning Using SAS Viya 3.4 requires significant effort and dedication. To pass the test and earn your certification, you need to put in a lot of hard work. That’s why we’ve created this study guide to help you prepare. By following these recommendations, you’ll master the information and skills needed for your desired certification. Additionally, we’ve compiled a list of the best learning resources to help you acquire the knowledge aligned with the main exam objectives outlined in this guide.

1. SAS Website

SAS website is the answer to excel in in-demand skills. It offers all possible aids needed for your course journey. As an international platform, it offers online learning opportunities. The website provides various training choices, including webinars and courses, to help you pursue your preferred course. You can earn certifications in various fields like Data Scientist, Statistical Business Analyst, and Predictive Modeler, ranging from basic to advanced programming.

2. SAS Training

The training offered by SAS is the most important part. You can use this Self e-learning course as per your advantage. You will learn the skills of Data Sources, Building Models, and Model Assessment and Deployment. In addition to that, you will gain knowledge of Neural Networks, Support Vector Machines, and Additional Topics. Lastly, you’ll also gain command of Decision Trees and Ensembles of Trees.

3. Reference Books

Without a doubt, books are the most dependable buddy you can have when studying for any exam. Books are your constant companions and are constantly at your disposal to help you solve your concerns and expand your knowledge. The SAS Institute also suggests a few publications that can assist you in achieving your goal of being a SAS Certified Specialist in Machine Learning Using SAS Viya 3.4.

• Machine Learning with SAS: Special Collection – This special collection will help you with Machine Learning patterns in data and models. Curated by Saratendu Sethi and published by SAS Institute itself.

• Applied Analytics Through Case Studies Using SAS and R – By Implementing Predictive Models and Machine Learning Techniques. It helps you to use Machine Learning to examine business problems and practical analytic approach. 

4. Join Online Community

Joining an online community, regardless of where it is done, is always good. When a large number of individuals get involved in a problem, the chances of finding a solution grow dramatically. In addition, having different points of view makes the material more lively. The research get more extensive as a result of these conversations. Introverts, who may normally avoid dialogues, get an opportunity to express themselves. Forums are excellent for forming a community that is necessary for understanding others.

5. Practice Tests

Taking a practice exam is a terrific method to mix up your study routine and guarantee that you get the greatest scores on the real thing. Analyzing your answers will help you find areas where you need to focus your efforts and will also reveal if you are on track to meet the exam goals. So start practicing for the SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 exam Now!

Enrich Your Skills and Knowledge to Become a SAS Certified Specialist in Machine Learning Using SAS Viya 3.4 with Hundreds of Practice Exams. Start Practicing Now!

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So, first let’s start with basics of the exam and cover the overview part.

Machine Learning Using SAS Viya 3.4: Exam Overview

SAS Machine Learning Using SAS Viya 3.4 exam requires to showcase your AI and Analytics Talent using Open Source and SAS tools to garner insight from data. This exam tests your knowledge of Visual Data Mining and Machine Learning software and skills such as,

• Firstly, preparing data and feature engineering
• Secondly, creating supervised machine learning models
• Thirdly, assessing model performance
• Lastly, deploying models into production

Your must know that this exam is good if you want to become data scientists for creating supervised machine learning models using pipelines in SAS Viya.

Quick Cheat Sheet for SAS Certified Specialist: Machine Learning Using SAS Viya 3.4

The SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 exam holds global recognition, significantly enhancing the value of your resume by validating your skills and knowledge. Achieving success in this exam requires substantial dedication, passion, effort, and time. However, with the right resources and training, you can effectively prepare and excel in the SAS exam. Without further delay, let’s explore the essential resources to facilitate a quick revision.

Understanding Exam Topics

The exam objectives for SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 helps you get in-depth details about the methods, components, resources, and the exam description. Moreover, with having a thorough analysis of the exam concepts will let you align yourself more deeply with the major objectives of the exam. As a result, you will also be able to review and mark the sections and topics you find difficult for studying later. However, the topics that are included in this exam are provided below:

Data Sources (30%)

• Create a project in Model Studio (Reference: Creating a New Project)
• Explore the data (Reference: Easing into Data Exploration, Reporting, and Analytics Using SAS Enterprise Guide)

• Modify data (Reference: MODIFY Statement)
• Reduce the dimensionality of the data (Reference: Multivariate Analysis: Principal Component Analysis)
• Use the VARIABLE SELECTION node to identify important variables (Reference: Variable Selection Node)

Building Models (50%)

• Describe key supervised machine learning terms and concepts (Reference: Machine Learning)
• Build models with decision trees and ensemble of trees (Reference: Tree-Based Machine Learning Methods in SAS Viya)
• Building models with neural networks (Reference: How to build deep learning models with SAS)
• Build models with support vector machines (Reference: Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification)
• Use Model Interpretability tools to explain black box models (Reference: Explaining complex models in SAS Viya with programmatic interpretability)
• Incorporate externally written code (Reference: Registering an External File with User-Written Code)

Model Assessment and Deployment (20%)

• Explain the principles of Model Assessment (Reference: Model assessment and selection in machine learning)
• Assess and compare models in Model Studio (Reference: Compare Models)
• Deploy a model (Reference: How to deploy and run your model with SAS Model Manager to the SAS Micro Analytic Score Service)

SAS Official Website 

Visiting the SAS official website is an important step while preparing for the Machine Learning exam. However, the official site offers a lot of reliable information and sources for good exam preparation. There you can find the resources such as study guide, documentation, sample papers, flashcards, whitepapers and FAQs. Moreover, this will help you in staying up to date with the latest exam modifications and changes.

SAS Training Program 

Training programs by SAS are a very necessary step in the preparation of such exams like SAS Certified Specialist: Machine Learning Using SAS Viya 3.4. SAS offers its own training programs on their various examinations and certifications. These training courses provide a solid theoretical foundation for various techniques related to supervised machine learning models. Additionally, they incorporate a business case study designed to lead you through all stages of the analytical life cycle. This encompasses understanding the problem, data preparation, feature selection, model training and validation, as well as model assessment and deployment.

• Machine Learning Using SAS Viya

SAS Flashcards

Studying flashcards will test your knowledge about the SAS Viya 3.4 exam with quiz-style printable flashcards. And, the study flashcards highlight the most critical learning points from the SAS Viya 3.4 study guide.

Using Books for better understanding

Books are often the initial resource that springs to mind when gearing up for an exam. They are easily accessible, allowing us to select books based on our convenience. You can explore numerous titles available at online stores, bookstores, or libraries based on your comprehension level. For this exam, some recommended books are:

• Exploring SAS Viya: Visual Analytics, Statistics, and Investigations
• Machine Learning with SAS: Special Collection

Practice Test 

Engaging in practice tests is crucial during exam preparation, offering insights into both your strengths and weaknesses. Effective time management is vital during the exam, and practicing helps enhance your answering skills, ultimately saving valuable time. It’s advisable to begin practice tests after completing a specific topic, serving as a valuable revision exercise.

Get Certified by passing SAS Certified Specialist: Machine Learning Using SAS Viya 3.4 Exam

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