SASInstitute A00-485 Exam : Modeling Using SAS Visual Statistics

A00-485
  • Exam Code: A00-485
  • Exam Name: Modeling Using SAS Visual Statistics
  • Updated: Aug 03, 2026
  • Q & A: 190 Questions and Answers

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SASInstitute A00-485 Exam Syllabus Topics:

SectionObjectives
Topic 1: Machine Learning Methods in Visual Statistics- Clustering techniques
- Decision trees and random forests concepts
Topic 2: Model Evaluation and Interpretation- Assessing model performance
- Interpreting model outputs and reports
Topic 3: Data Preparation and Exploration in SAS Viya- Exploratory data analysis and visualization
- Loading and preparing data for Visual Statistics
Topic 4: Predictive Modeling Fundamentals- Regression modeling concepts
  • 1. Logistic regression
    • 2. Linear regression
      - Model selection and comparison techniques

      SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

      1. Refer to the exhibit:

      A retailer wants to use a decision tree to select one segment to mail 35,000 coupons to existing customers for a new line of organic products.
      Assuming that customers who have purchased organic products in the past are likely to want new organic products in the future, select the single best Leaf ID for the mailing.

      A) 15
      B) 12
      C) 17
      D) 18


      2. Which SAS tool is most suitable for managing explorations and visualizations in a business intelligence context?

      A) SAS Enterprise Miner
      B) SAS Studio
      C) SAS Visual Data Builder
      D) SAS Enterprise Guide


      3. Refer to the exhibit:

      Based on the Validation Data in the Model Comparison output which conclusions are valid? (Choose two.)

      A) Decision Tree model would be selected at lift=5
      B) Decision Tree model would be selected at lift=20
      C) Logistic Regression model would be selected at lift=20
      D) Logistic Regression model would be selected at lift=5


      4. What does adding an interaction effect in linear regression modeling allow you to capture?

      A) The effect of missing data on the model
      B) The multicollinearity among predictors
      C) The joint impact of two or more predictors on the response variable
      D) The presence of outliers in the data


      5. Refer to the exhibits:
      Exhibit 1:

      Exhibit 2:

      An analyst has created a cluster model based on the settings in Exhibit 1 and chose to derive a cluster ID variable.
      Why are there 5 cluster IDs in Exhibit 2 based on these settings?

      A) There are observations with missing values.
      B) Parallel Coordinates Visible Roles setting is 5.
      C) Cluster Matrix Visible Roles setting is 5.
      D) Variable Standardization has been chosen.


      Solutions:

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

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