SASInstitute A00-485 : Modeling Using SAS Visual Statistics

A00-485 real exams

Exam Code: A00-485

Exam Name: Modeling Using SAS Visual Statistics

Updated: Jul 29, 2026

Q & A: 190 Questions and Answers

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SASInstitute A00-485 Exam Syllabus Topics:

SectionObjectives
Predictive Modeling Fundamentals- Regression modeling concepts
  • 1. Logistic regression
    • 2. Linear regression
      - Model selection and comparison techniques
      Data Preparation and Exploration in SAS Viya- Exploratory data analysis and visualization
      - Loading and preparing data for Visual Statistics
      Machine Learning Methods in Visual Statistics- Decision trees and random forests concepts
      - Clustering techniques
      Model Evaluation and Interpretation- Interpreting model outputs and reports
      - Assessing model performance

      SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

      1. What information can you gather from Leaf statistics in a decision tree analysis?

      A) The total number of data points in each leaf node
      B) The tree's overall accuracy and error rate
      C) The tree's decision rules for each leaf node
      D) The distribution of target variable values in each leaf node


      2. When filtering data for a model in SAS Visual Analytics, what can you do to exclude selections and filter data?

      A) Define a filter condition using the interactive filter pane
      B) Apply a data filter on the visualization
      C) Specify exclusion criteria in the data source properties
      D) Use the exclude option when setting up a filter


      3. Which statement is true for the Informative Missingness property in SAS Visual Statistics?

      A) Missing values are flagged on diagnostic plots.
      B) The user is prompted to fill in a value to impute for missing predictor variables.
      C) Missing values are imputed and indicator variables are created for missing predictors.
      D) A table of missingness patterns for the predictor variables is displayed in the model output.


      4. How can score code be implemented to score new data using SAS Enterprise Miner?

      A) By creating a scoring node in a SAS Enterprise Miner process flow
      B) By pasting the code into a web browser
      C) By importing the code into SAS Data Integration Studio
      D) By manually executing the code in a command-line interface


      5. When creating a Linear Regression, Category Variables can be used for Classification Effects and

      A) Frequency
      B) Response
      C) Group-by
      D) Continuous Effects


      Solutions:

      Question # 1
      Answer: A,D
      Question # 2
      Answer: A,B,C,D
      Question # 3
      Answer: C
      Question # 4
      Answer: A
      Question # 5
      Answer: C

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