SASInstitute : A00-402 Questions & Answers
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SAS Certified Specialist (A00-402) is the specialist SAS certification for business intelligence developers and data analysts to help them build and implement BI solutions with SAS® software. SAS Certified Specialist (A00-402) certifications are designed to validate skills required to build and implement BI solutions using SAS software. SAS is a well-known analytics software used by many large businesses. SAS Institute A00-402 Dumps will help you learn the skills you need to pass this exam. In order to become a SAS administrator, you need to pass the SAS Certified Specialist A00-402 exam. This is your guide to help you get there!
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Candidates working across multiple industries can take up this certification to validate their skills. There are several ways available to become SAS Certified Specialist certified candidate. These include taking paid training courses from a few commercial vendors or enrolling in university programs that are dedicated to preparing students for this certification.
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The SAS Certified Specialist certification is a credential that has been designed to assess an individual's ability in using SAS software tools, applications, and procedures. It is a professional-level certification that validates the candidates' expertise in SAS programming.
SASInstitute A00-402 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Overview of Supervised Machine Learning | 10-15% | - Basic concepts and terminology - Model overfitting, underfitting, and generalization - Prediction types and modeling goals |
| Topic 2: Model Assessment and Comparison | 15-20% | - Classification metrics: accuracy, precision, recall, F1, AUC - Interpreting model results and diagnostics - Profit/loss analysis and cutoff adjustment - Comparing models and selecting best performer - Regression metrics: RMSE, R-squared, MAE |
| 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% | - Variable selection and reduction - Partitioning data into training, validation, and test sets - Handling missing values and outliers - Loading and accessing data sources - Data profiling and exploration - Feature engineering and transformation |
| Topic 5: Building Predictive Models | 25-30% | - Decision trees and tree ensembles - Regression models (linear, logistic) - Regularization and optimization methods - Using appropriate modeling nodes - Neural networks and deep learning basics |
| Topic 6: Creating and Managing Pipelines | 15-20% | - Managing pipeline flow and execution - Configuring and connecting nodes - Building pipelines in Model Studio - Using pipeline templates and automation |
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