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SASInstitute A00-402: SAS Viya 3.5 Supervised Machine Learning Pipelines exam is a globally recognized certification that validates the candidate's knowledge and skills in building and deploying machine learning models using SAS Viya 3.5. SAS Viya 3.5 Supervised Machine Learning Pipelines certification is highly valued by employers and can help professionals advance their careers in the field of data science and machine learning. Candidates can prepare for the exam by taking advantage of the various training courses and practice exams offered by SAS.
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SASInstitute A00-402 exam is a vital certification exam that aims to test individuals’ proficiency in the area of SAS Viya 3.5 Supervised Machine Learning Pipelines. A00-402 exam is designed to test the skills needed to manage, prepare and analyze data, construct predictive models using machine learning algorithms, and deploy models into production environments. Aspiring data scientists, business analysts, and machine learning engineers can take A00-402 exam to demonstrate their skills in this area and receive recognition for their expertise.
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Earning the SASInstitute A00-402 certification is a great way to validate your expertise in using the SAS Viya 3.5 supervised machine learning pipeline, and to demonstrate your commitment to continuous learning and professional development. SAS Viya 3.5 Supervised Machine Learning Pipelines certification is recognized worldwide as a highly respected credential in the field of data science and analytics, and it is often a requirement for many job openings in this area. By passing the A00-402 exam, you can showcase your skills to potential employers and clients and set yourself apart from your peers in the industry.
SASInstitute A00-402 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Overview of Supervised Machine Learning | 10-15% | - Basic concepts and terminology - Prediction types and modeling goals - Model overfitting, underfitting, and generalization |
| Topic 2: Building Predictive Models | 25-30% | - Regularization and optimization methods - Decision trees and tree ensembles - Regression models (linear, logistic) - Neural networks and deep learning basics - Using appropriate modeling nodes |
| Topic 3: Model Deployment | 5-10% | - Exporting score code - Registering and publishing models - Model scoring and operationalization |
| Topic 4: Data Preparation and Exploration | 20-25% | - Partitioning data into training, validation, and test sets - Variable selection and reduction - Loading and accessing data sources - Feature engineering and transformation - Handling missing values and outliers - Data profiling and exploration |
| Topic 5: Creating and Managing Pipelines | 15-20% | - Managing pipeline flow and execution - Building pipelines in Model Studio - Using pipeline templates and automation - Configuring and connecting nodes |
| Topic 6: Model Assessment and Comparison | 15-20% | - Comparing models and selecting best performer - Interpreting model results and diagnostics - Profit/loss analysis and cutoff adjustment - Classification metrics: accuracy, precision, recall, F1, AUC - Regression metrics: RMSE, R-squared, MAE |
