SASInstitute A00-255 dumps - in .pdf

A00-255 pdf
  • Exam Code: A00-255
  • Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
  • Updated: Aug 01, 2026
  • Q & A: 87 Questions and Answers
  • PDF Price: $59.99

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  • Exam Code: A00-255
  • Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
  • Updated: Aug 01, 2026
  • Q & A: 87 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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SASInstitute A00-255 dumps - Testing Engine

A00-255 Testing Engine
  • Exam Code: A00-255
  • Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
  • Updated: Aug 01, 2026
  • Q & A: 87 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

About SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 : A00-255 Exam Questions

SASInstitute A00-255 certification is a valuable credential for professionals who want to advance their careers in data science, business analytics, and related fields. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification demonstrates the candidates' expertise in using SAS Enterprise Miner to perform predictive modeling tasks, which is a critical skill in today's data-driven business environment. Holding the A00-255 certification can also lead to better job opportunities and higher salaries.

SASInstitute A00-255 Certification Exam consists of 70 multiple-choice and short-answer questions that need to be completed within 2 hours and 30 minutes. The topics covered in the exam include data preparation, variable selection, model assessment, and deployment. A00-255 exam also tests the candidate's ability to interpret results and communicate findings to stakeholders. A00-255 exam is available in English and can be taken online or in person at SASInstitute authorized testing centers.

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SASInstitute A00-255 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Predictive Model Assessment and Implementation25-30%- Model Evaluation
  • 1. Use decision processing for oversampling adjustment
  • 2. Select appropriate fit statistics
  • 3. Compare models using Model Comparison node
  • 4. Assess model performance using profit and loss information
- Model Deployment
  • 1. Score data sets within Enterprise Miner
Topic 2: Data Sources20-25%- Prepare Source Data
  • 1. Prepare data for predictive modeling
  • 2. Modify source data
- Create and Explore Data Sources
  • 1. Create data sources from SAS tables in Enterprise Miner
  • 2. Explore and assess data sources
Topic 3: Building Predictive Models35-40%- Model Development
  • 1. Describe predictive modeling concepts and terminology
  • 2. Build neural network models
  • 3. Build decision tree models
  • 4. Build regression models
Topic 4: Pattern Analysis10-15%- Segmentation and Association Analysis
  • 1. Perform sequence analysis and market basket analysis
  • 2. Perform association analysis
  • 3. Identify clusters using Cluster and Segment Profile nodes

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