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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Scoring and Deployment | 20% | - Model scoring
- Model deployment
|
| Topic 2: Building Predictive Models | 40% | - Decision trees
- Regression models
- Gradient boosting
- Neural networks
|
| Topic 3: Data Preparation and Exploration | 20% | - Data exploration using SAS Visual Statistics
- Importing data
- Data quality
|
| Topic 4: Model Assessment and Selection | 20% | - ROC and Lift curves
- Model fit statistics
- Model comparison
|
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. When reviewing outlier details in linear regression, what factors should be considered when deciding whether to exclude outliers?
A) The variable roles assigned to outliers
B) The p-values associated with the outliers
C) The magnitude of the outlier and its impact on the model
D) The number of outliers in the dataset
2. What information can you interpret from a Cluster Matrix?
A) The order of cluster creation
B) The cluster centroids
C) The number of data points in each cluster
D) The proximity or similarity between clusters
3. What is the primary purpose of scoring functionality in SAS Visual Statistics?
A) To assess the distribution of predictor variables
B) To select the best model for a given dataset
C) To evaluate model performance on a test dataset
D) To generate code for deploying models in production
4. Interactions can be created and added to a linear regression model.
Which statement is true about interactions?
A) An interaction can be created for a squared category variable.
B) Each interaction is limited to 3 effects or fewer.
C) The maximum number of interactions allowed in a model is 3.
D) Measures and categories can be combined to create an interaction.
5. Which statement is true regarding model comparison within SAS Visual Statistics?
A) A logistic regression and a decision tree cannot be compared.
B) Two cluster analyses cannot be compared if they have different initial seed assignments.
C) Two generalized linear models cannot be compared if they use different underlying distributions.
D) A linear regression and a generalized linear model cannot be compared.
Solutions:
Question # 1 Answer: C | Question # 2 Answer: B,D | Question # 3 Answer: C,D | Question # 4 Answer: D | Question # 5 Answer: B |