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SAS Institute A00-402 Exam is a computer based test which is conducted by PEARSON VUE. It is a test on the fundamentals of programming in SAS Language. In this exam, you will be tested on your knowledge of the SAS language and its use. This includes your ability to code, design and create reports and charts, along with handling and modifying data in a database. It is required that the candidate should have an experience of 3 months or more as a SAS programmer before sitting for this exam. Since he/she should have worked with SAS on regular basis, he/she should be able to grasp the concepts quickly and effectively. The candidate can enroll themselves for the same once they have cleared the SAS Language first level test. SAS Institute A00-402 Dumps will help you prepare for the same. The SAS Language first level exam covers an overview of SAS Basics such as creating formulas, writing procedures, building classes etc. Once you are clear about the basics then it is time for you to enroll for this exam and attempt it without much preparation.

Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/machine-learning-specialist.html

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What is the salary of an SAS Certified Specialist certified professional?

The Average salary of different countries of SAS Certified Specialist Certified professional

  • UK - 45984 Pounds
  • India - 4616507 INR
  • United States - $61000 USD

SASInstitute A00-402 Exam Syllabus Topics:

SectionWeightObjectives
Overview of Supervised Machine Learning10-15%- Prediction types and modeling goals
- Model overfitting, underfitting, and generalization
- Basic concepts and terminology
Creating and Managing Pipelines15-20%- Configuring and connecting nodes
- Managing pipeline flow and execution
- Using pipeline templates and automation
- Building pipelines in Model Studio
Data Preparation and Exploration20-25%- Data profiling and exploration
- Feature engineering and transformation
- Partitioning data into training, validation, and test sets
- Handling missing values and outliers
- Variable selection and reduction
- Loading and accessing data sources
Model Deployment5-10%- Exporting score code
- Model scoring and operationalization
- Registering and publishing models
Model Assessment and Comparison15-20%- Regression metrics: RMSE, R-squared, MAE
- Comparing models and selecting best performer
- Classification metrics: accuracy, precision, recall, F1, AUC
- Profit/loss analysis and cutoff adjustment
- Interpreting model results and diagnostics
Building Predictive Models25-30%- Neural networks and deep learning basics
- Regression models (linear, logistic)
- Regularization and optimization methods
- Using appropriate modeling nodes
- Decision trees and tree ensembles

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