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SAS Certified Specialist (A00-402) Exam Format
Language: English
Passing Score: Must achieve a score of 65 percent correct to pass
Exam Length: 50-55 Questions
Exam Format: Multiple choice and Short-answer questions
Exam Duration: 100 minutes to complete the exam.
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20-30 hours’ preparation before the A00-402 exam
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What are the Registration Procedure of SAS Institute A00-402 Exam?
The first step is to go on the web page of Pearson VUE. It is the official website of Pearson VUE and it will help you in getting information about the registration procedure, dates, and other details. It is very important to have all these details before going to take the exam because if any mistake occurs, then you may not be able to take it at all. Once you are on the website, you must follow certain steps and fill up some details in order to get registered for this exam. All these details are mentioned on the website and you just have to follow them as instructed. Once your registration is completed, you will be given a unique ID number that will be used during your examination which will help you in logging into your account.
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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: Model Assessment and Comparison | 15-20% | - Regression metrics: RMSE, R-squared, MAE - Comparing models and selecting best performer - Profit/loss analysis and cutoff adjustment - Interpreting model results and diagnostics - Classification metrics: accuracy, precision, recall, F1, AUC |
| Topic 3: Building Predictive Models | 25-30% | - Using appropriate modeling nodes - Regularization and optimization methods - Decision trees and tree ensembles - Regression models (linear, logistic) - Neural networks and deep learning basics |
| Topic 4: Model Deployment | 5-10% | - Model scoring and operationalization - Registering and publishing models - Exporting score code |
| Topic 5: Creating and Managing Pipelines | 15-20% | - Using pipeline templates and automation - Managing pipeline flow and execution - Building pipelines in Model Studio - Configuring and connecting nodes |
| Topic 6: Data Preparation and Exploration | 20-25% | - Variable selection and reduction - Loading and accessing data sources - Handling missing values and outliers - Partitioning data into training, validation, and test sets - Feature engineering and transformation - Data profiling and exploration |





