SASInstitute Modeling Using SAS Visual Statistics - A00-485

SASInstitute A00-485 test insides dumps
  • Exam Code: A00-485
  • Exam Name: Modeling Using SAS Visual Statistics
  • Updated: Aug 16, 2026
  • Q & A: 190 Questions and Answers
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SASInstitute A00-485 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: Building and Assessing Segmentation Models28%- Perform unsupervised segmentation using cluster analysis
- Assess and interpret decision tree performance
- Analyze and interpret cluster results
- Perform supervised segmentation using decision trees
Topic 2: Model Comparison and Scoring9%- Apply models to score new data
- Compare multiple models using fit statistics
Topic 3: Building and Assessing Regression-type Models41%- Perform generalized linear regression modeling
- Explain concepts of linear models
- Perform nonparametric logistic regression modeling
- Assess model fit and diagnostics
- Perform linear regression modeling
- Perform generalized additive modeling
Topic 4: SAS Visual Statistics Cross-functional Tasks22%- Prepare data using SAS Visual Analytics
- Filter data used for a model
- Perform model validation
- Use interactive group-by functionality
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. Prior to fitting a regression model, data binning of a continuous effect should be used for what purpose?

A) Improve the accuracy of your predictions.
B) Reduce the impact of minor observation errors.
C) Gain information due to discretization.
D) Reduce the cardinality of a continuous effect.


2. 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=5
D) Logistic Regression model would be selected at lift=20


3. How can predicted values be derived and described in terms of predicted probabilities in SAS Visual Statistics?

A) By applying the link function to the linear predictor
B) By summing the residuals of the model
C) By calculating the mean of the predictor variables
D) By fitting a linear regression model to the data


4. Given a scenario where the response variable represents the number of accidents in a day, what distribution and link function might be appropriate for modeling?

A) Logistic distribution with a logit link function
B) Exponential distribution with a square root link function
C) Normal distribution with an identity link function
D) Poisson distribution with a log link function


5. In SAS Visual Statistics, how can you use a derived cluster ID variable in another visualization?

A) By embedding it within a scatter plot
B) By linking it to a table in a database
C) By exporting it as a separate dataset
D) By including it as a dimension in a new visualization


Solutions:

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

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