The SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 exam stands as a gateway to unlocking your potential in the booming field of machine learning. In today’s data-driven world, organizations are actively seeking professionals with the expertise to extract valuable insights and predictions from their information. This certification validates your proficiency in using SAS Viya 4.0, a cutting-edge platform, to build and deploy powerful machine-learning models.
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.
