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SAS Certified Specialist (A00-402) Exam Format

  • Exam Duration: 100 minutes to complete the exam.

  • Exam Length: 50-55 Questions

  • Language: English

  • Passing Score: Must achieve a score of 65 percent correct to pass

  • Exam Format: Multiple choice and Short-answer questions

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

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Invest in your future by earning a SAS Certified Specialist (A00-402) Certification

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!

SASInstitute A00-402 Exam Syllabus Topics:

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

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