Exam Name: Modeling Using SAS Visual Statistics
Certification Provider: SASInstitute
Corresponding Certification: SASInstitute Certification
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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and Assessing Regression-type Models | 41% | - Perform generalized linear regression modeling - Perform generalized additive modeling - Perform linear regression modeling - Perform nonparametric logistic regression modeling - Assess model fit and diagnostics - Explain concepts of linear models |
| Topic 2: SAS Visual Statistics Cross-functional Tasks | 22% | - Perform model validation - Filter data used for a model - Prepare data using SAS Visual Analytics - Use interactive group-by functionality |
| Topic 3: Building and Assessing Segmentation Models | 28% | - Analyze and interpret cluster results - Perform supervised segmentation using decision trees - Perform unsupervised segmentation using cluster analysis - Assess and interpret decision tree performance |
| Topic 4: Model Comparison and Scoring | 9% | - Compare multiple models using fit statistics - Apply models to score new data |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
What is the purpose of setting Parallel Coordinate properties for a cluster in SAS Visual Statistics?
- A. To assign labels to the cluster's variables
- B. To create a scatter plot of data points
- C. To visualize the distribution of data within the cluster
- D. To determine the cluster's centroid
Correct Answer: C 🗳️
Which SAS tool is suitable for creating complex data manipulation and transformation workflows in a visual interface?
- A. SAS Visual Analytics
- B. SAS Studio
- C. SAS Visual Data Builder
- D. SAS Enterprise Guide
Correct Answer: C 🗳️
What is variable selection in SAS Visual Statistics used for in logistic regression modeling?
- A. To exclude all predictor variables
- B. To assign roles to variables
- C. To specify the distribution of the response variable
- D. To identify and choose relevant predictor variables
Correct Answer: D 🗳️
How does SAS Data Integration Studio facilitate scoring new data using exported score code?
- A. By generating score code automatically during ETL processes
- B. By providing a visual interface for connecting to external data sources
- C. By enabling the scheduling of batch scoring jobs
- D. By automatically tuning hyperparameters for scoring
Correct Answer: A,C 🗳️
What is meant by "informative missingness" in the context of linear regression?
- A. Missing data that provides valuable information about the outcome
- B. Missing data that leads to multicollinearity among predictors
- C. Missing data that is unrelated to the outcome variable
- D. Missing data that is randomly distributed across observations
Correct Answer: A 🗳️
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