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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Scoring and Deployment | 20% | - Model deployment - Model scoring |
| Building Predictive Models | 40% | - Neural networks - Regression models - Decision trees - Gradient boosting |
| Data Preparation and Exploration | 20% | - Importing data - Data exploration using SAS Visual Statistics - Data quality |
| Model Assessment and Selection | 20% | - Model fit statistics - ROC and Lift curves - Model comparison |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
What can you interpret from the Assessment panel in a decision tree analysis?
- A. The tree's training and validation performance metrics
- B. The feature importance for each variable in the tree
- C. The tree's structural properties, such as depth and number of nodes
- D. The decision boundaries between leaf nodes
Correct Answer: A 🗳️
The Leaf size property within a decision tree is changed from 5 to 10 and no other stopping rules were met.
How will the tree change?
- A. The tree will be harder to interpret.
- B. The tree will be more complex.
- C. The tree will train more slowly.
- D. The tree will be simpler.
Correct Answer: D 🗳️
Given a scenario where the response variable represents the time until an event occurs, what distribution and link function might be appropriate for modeling?
- A. Logistic distribution with a logit link function
- B. Exponential distribution with a log link function
- C. Poisson distribution with a log link function
- D. Normal distribution with an identity link function
Correct Answer: B 🗳️
When specifying Cluster Roles for Cluster Analysis in SAS Visual Statistics, which condition is correct?
- A. Category items are not allowed.
- B. A target variable is required.
- C. At least two variables are required.
- D. Interaction items are allowed.
Correct Answer: C 🗳️
What is the purpose of implementing score code generated by SAS Visual Statistics?
- A. To evaluate the performance of the scoring code
- B. To calculate the p-values of predictor variables
- C. To apply the trained model to new data for predictions
- D. To assess the distribution of residuals in the model
Correct Answer: C 🗳️
