SASInstitute A00-485 exam dumps - Modeling Using SAS Visual Statistics

  • Exam Code: A00-485
  • Exam Name: Modeling Using SAS Visual Statistics
  • Updated: Oct 20, 2026     Q & A: 190 Questions and Answers

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SASInstitute A00-485 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Scoring and Deployment20%- Model deployment
- Model scoring
Topic 2: Model Assessment and Selection20%- Model comparison
- ROC and Lift curves
- Model fit statistics
Topic 3: Data Preparation and Exploration20%- Data exploration using SAS Visual Statistics
- Data quality
- Importing data
Topic 4: Building Predictive Models40%- Regression models
- Neural networks
- Decision trees
- Gradient boosting

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

Question #1

Refer to the exhibit from SAS Visual Statistics:

What can be concluded from the lift plot shown in the exhibit?

  • A. The response rate for the best 20% is predicted to be 2.75 times greater than the sample fitted in the model.
  • B. The 20% of cases with the lowest predicted probability of the modeled event level is predicted to capture 2.75 times as many events as a random sample of 20%.
  • C. The 20% of cases with the highest predicted probability of the modeled event level is expected to capture 2.75 times as many events as a random sample of the same size.
  • D. The response rate for the best 20% is predicted to increase 5 times under the fitted model.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #2

Which statement is true about Decision Trees in SAS Visual Statistics

  • A. Decision trees can be used for outlier detection.
  • B. Decision trees can be used for classification and estimation.
  • C. Decision trees cannot handle continuous dependent variables.
  • D. Decision trees cannot handle missing values.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

Refer to the exhibit:

An economist is predicting demand using a linear regression model.
If all of the effect variables equal 1, what is the predicted demand?

  • A. 362.415133
  • B. 352.454433
  • C. 352.405432
  • D. 352.415433
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #4

What is meant by "informative missingness" in the context of linear regression?

  • A. Missing data that is randomly distributed across observations
  • B. Missing data that is unrelated to the outcome variable
  • C. Missing data that provides valuable information about the outcome
  • D. Missing data that leads to multicollinearity among predictors
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #5

Refer to the exhibit:

Prior to performing a decision tree analysis, you need to assess the default Values of Classification and Model Type Properties for Sale Amount. The variable represents product purchase amounts from an organization's e-commerce website.
How do you want the decision tree algorithm to treat this variable?

  • A. Classification = Measure & Model Type = Continuous
  • B. Classification = Category & Model Type = Discrete
  • C. Classification = Measure & Model Type = Discrete
  • D. Classification = Category & Model Type = Continuous
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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