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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Machine Learning Methods in Visual Statistics | - Decision trees and random forests concepts - Clustering techniques |
| Topic 2: Data Preparation and Exploration in SAS Viya | - Loading and preparing data for Visual Statistics - Exploratory data analysis and visualization |
| Topic 3: Predictive Modeling Fundamentals | - Model selection and comparison techniques - Regression modeling concepts
|
| Topic 4: Model Evaluation and Interpretation | - Assessing model performance - Interpreting model outputs and reports |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. Why are decision trees a useful technique for segmentation?
A) Trees treat each split cell dependently based on earlier split decisions.
B) Trees split the response into heterogeneous groups, while minimizing the difference in the response of the groups.
C) Trees split the response into homogeneous groups, while maximizing the difference in the response of the groups.
D) Trees split data into smaller cells which are decreasingly "pure".
2. In a generalized linear model, what is the role of the link function in transforming the linear predictor?
A) It determines the distribution of the response variable.
B) It scales the predictor variables.
C) It ensures that the linear predictor is always positive.
D) It maps the linear predictor to the range of the response variable.
3. Which of the following tasks in SAS Visual Analytics involves reducing the granularity of categorical data?
A) Creating a dummy variable
B) Imputing a variable
C) Transforming a variable
D) Aggregating a measure
4. What is meant by "informative missingness" in the context of linear regression?
A) Missing data that leads to multicollinearity among predictors
B) Missing data that is randomly distributed across observations
C) Missing data that is unrelated to the outcome variable
D) Missing data that provides valuable information about the outcome
5. What is the primary purpose of imputing a variable in SAS Visual Analytics?
A) To aggregate data
B) To replace incorrect values
C) To remove missing values
D) To create new variables
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
