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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Selection | 20% | - Model fit statistics - Model comparison - ROC and Lift curves |
| Scoring and Deployment | 20% | - Model deployment - Model scoring |
| Data Preparation and Exploration | 20% | - Importing data - Data exploration using SAS Visual Statistics - Data quality |
| Building Predictive Models | 40% | - Neural networks - Regression models - Decision trees - Gradient boosting |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. What are some properties that can be defined in a linear regression model?
A) Learning rate and number of iterations
B) Maximum depth and minimum leaf size
C) Confusion matrix and accuracy
D) Coefficients, p-values, and R-squared
2. Refer to the exhibit from a linear regression model in SAS Visual Statistics.
Based on the table above and assuming a significance level of 0.05, what can be concluded about the linear regression model?
A) For a .03696 unit decrease in RunPulse, there is an expected one-unit increase in the response.
B) RestPulse is a significant predictor of the response.
C) For one one-unit increase in RunTime, there is an expected increase in the response of 2.6287.
D) The Intercept is an important predictor of the response.
3. When evaluating a Misclassification plot, what does it typically show?
A) The trade-off between false positives and false negatives at different thresholds
B) The p-values associated with the predictor variables
C) The distribution of predictor variables in the dataset
D) The relationship between two predictor variables
4. How can you interpret model results using the advanced group-by feature in SAS Visual Analytics?
A) Analyze the impact of different levels of the group-by variable on the model outcome
B) Explore interaction effects between the group-by variable and other predictors
C) Visualize the predicted values for each level of the group-by variable
D) Review the model coefficients for each level of the group-by variable
5. What conditions might make models less comparable in SAS Visual Statistics?
A) If they have similar AUC values
B) If they have different learning rates
C) If they are both logistic regression models
D) If they use the same dataset and response variable
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A,B,C,D | Question # 5 Answer: A,B |
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