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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Selection | 20% | - Model fit statistics - ROC and Lift curves - Model comparison |
| Topic 2: Building Predictive Models | 40% | - Gradient boosting - Neural networks - Regression models - Decision trees |
| Topic 3: Scoring and Deployment | 20% | - Model deployment - Model scoring |
| Topic 4: Data Preparation and Exploration | 20% | - Data exploration using SAS Visual Statistics - Importing data - Data quality |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. What properties can be defined in a logistic regression model?
A) The distribution of the response variable
B) Coefficients and p-values
C) The learning rate
D) The number of iterations
2. What are the essentials of logistic regression?
A) Assuming a linear relationship between predictors and response
B) Modeling binary or ordinal outcomes
C) Using the logit link function to model the probability of an event
D) Estimating probabilities of categorical outcomes
3. What information is conveyed by a Summary bar in model assessment?
A) The distribution of residuals in the model
B) The standard deviation of the predictor variables
C) The summary statistics of the response variable
D) The performance metrics of the model, such as AUC and KS Statistic
4. Interactive group-by analysis can be performed for each of the following in Visual Statistics except:
A) Decision tree
B) Generalized linear model
C) Linear regression
D) Logistic regression
5. Which model does not produce score code?
A) Regression using interaction effects
B) Regression using the group by option
C) Decision Tree using interactive mode
D) Decision Tree using the rapid growth option
Solutions:
| Question # 1 Answer: A,B | Question # 2 Answer: B,C,D | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C |
