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SASInstitute A00-402 exam, also known as the SAS Viya 3.5 Supervised Machine Learning Pipelines exam, is designed to evaluate an individual's ability to implement, manage, and deploy supervised machine learning models using SAS Viya. SAS Viya is a cloud-native, open analytics platform that enables organizations to perform advanced analytics on a centralized platform.
SAS certification, one of the most recognized and respected professional certifications in the IT industry, provides objective measures that validate SAS® skills and competency. The SAS Certified Specialist (A00-402) exam validates the advanced SAS Programming and Administration skills of experienced SAS programmers who are creating or maintaining programs within an organization. Testers who obtain this certification have demonstrated a mastery of the programming and administration tasks that are necessary to develop, maintain, test and deploy business applications using SAS products. SAS Institute A00-402 Dumps provides you with detailed instructions on how to pass SAS Institute A00-402 test. Candidates for this exam should possess several years of experience developing SAS programs, including tasks related to data management, application development, user interface development, report generation, and database access and maintenance. Candidates should also have experience with installation of SAS products on UNIX® operating systems as well as installation of SAS products on Microsoft Windows operating systems.
The SASInstitute A00-402 exam covers a range of topics related to supervised machine learning, including data preparation, feature engineering, model selection and validation, and deployment of machine learning models. It also assesses the candidate's ability to use SAS Viya tools and technologies such as SAS Visual Data Mining and Machine Learning, SAS Studio, and SAS Model Manager.
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| Section | Weight | Objectives |
|---|---|---|
| Building Predictive Models | 25-30% | - Regularization and optimization methods - Using appropriate modeling nodes - Regression models (linear, logistic) - Decision trees and tree ensembles - Neural networks and deep learning basics |
| Model Deployment | 5-10% | - Model scoring and operationalization - Registering and publishing models - Exporting score code |
| Overview of Supervised Machine Learning | 10-15% | - Basic concepts and terminology - Prediction types and modeling goals - Model overfitting, underfitting, and generalization |
| Model Assessment and Comparison | 15-20% | - Classification metrics: accuracy, precision, recall, F1, AUC - Interpreting model results and diagnostics - Regression metrics: RMSE, R-squared, MAE - Comparing models and selecting best performer - Profit/loss analysis and cutoff adjustment |
| Creating and Managing Pipelines | 15-20% | - Managing pipeline flow and execution - Using pipeline templates and automation - Configuring and connecting nodes - Building pipelines in Model Studio |
| Data Preparation and Exploration | 20-25% | - Feature engineering and transformation - Data profiling and exploration - Handling missing values and outliers - Variable selection and reduction - Partitioning data into training, validation, and test sets - Loading and accessing data sources |
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