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SASInstitute A00-485 Exam Syllabus Topics:

SectionObjectives
Topic 1: Predictive Modeling Fundamentals- Regression modeling concepts
  • 1. Logistic regression
    • 2. Linear regression
      - Model selection and comparison techniques
      Topic 2: Data Preparation and Exploration in SAS Viya- Exploratory data analysis and visualization
      - Loading and preparing data for Visual Statistics
      Topic 3: Machine Learning Methods in Visual Statistics- Decision trees and random forests concepts
      - Clustering techniques
      Topic 4: Model Evaluation and Interpretation- Interpreting model outputs and reports
      - Assessing model performance

      SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

      Question 1

      Refer to the exhibit:

      Which option was not specified in creating the linear regression model using SAS Visual Statistics?

      A. continuous effects
      B. interaction term
      C. group-by variable
      D. variable selection


      Question 2

      What information can you interpret from a Cluster Matrix?

      A. The proximity or similarity between clusters
      B. The order of cluster creation
      C. The number of data points in each cluster
      D. The cluster centroids


      Question 3

      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 use the same dataset and response variable
      D. If they are both logistic regression models


      Question 4

      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. Exponential distribution with a log link function
      B. Normal distribution with an identity link function
      C. Logistic distribution with a logit link function
      D. Poisson distribution with a log link function


      Question 5

      In model assessment, why might you need to assess residuals and other model diagnostics to choose an appropriate distribution and link function?

      A. To ensure that predictor variables are not highly correlated
      B. To calculate the p-values of predictor variables
      C. To determine the order of predictor variables in the model
      D. To verify if the chosen model assumptions are met and make adjustments if necessary


      Solutions:

      Question 1
      Answer: C
      Question 2
      Answer: A,D
      Question 3
      Answer: A,B
      Question 4
      Answer: A
      Question 5
      Answer: D

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