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Preparation Guide of SAS Institute A00-225 Exam
A00-225 Prep Guide: Prep guide for the SAS Institute A00-225 Exam
An Analysis of the SAS Institute A00-225 Exam: A blog about the SAS Institute A00-225 exam along with preparation tips.
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The SAS Institute A00-225 exam is an exam that is designed to show that you have the knowledge and skills needed to use SAS software. You will need this certification if you use SAS software at work so that your employer can vouch for your skills and prove how valuable you are to the company.
SASInstitute A00-225 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS Advanced Predictive Modeling using SAS Enterprise Miner 14 |
| Exam Number: | A00-225 |
| Passing Score: | 70% |
| Exam Format: | Multiple choice |
| Real Exam Qty: | 60-65 |
| Available Languages: | English |
| Related Certifications: | SAS Certified Base Programmer for SAS 9 SAS Certified Data Scientist SAS Certified Advanced Programmer for SAS 9 |
| Exam Duration: | 135 minutes |
| Exam Price: | 180 USD |
| Recommended Training: | SAS Advanced Predictive Modeling using SAS Enterprise Miner (Official Training) |
| Exam Registration: | SAS Certification Portal Pearson VUE SAS Exams |
| Exam Way: | Online proctored or test center (Pearson VUE) |
| Pre Condition: | Recommended: SAS Certified Base Programmer for SAS 9 or equivalent SAS programming experience |
| Official Syllabus URL: | https://www.sas.com/en_us/certification.html |
SASInstitute A00-225 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Exploration and Preparation | - Feature selection and variable reduction techniques - Data cleaning, missing value handling, and transformations |
| SEMMA Methodology and Enterprise Miner Environment | - Understanding SEMMA process (Sample, Explore, Modify, Model, Assess) - SAS Enterprise Miner project workflow and nodes |
| Model Assessment and Validation | - Model comparison and selection strategies - Lift charts, ROC curves, and confusion matrix |
| Model Deployment Concepts | - Scoring data and operationalizing models |
| Clustering and Segmentation | - K-means clustering and hierarchical clustering |
| Predictive Modeling Techniques | - Neural networks and ensemble methods - Regression models (linear and logistic regression) - Decision trees and rule-based models |
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