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SASInstitute A00-485 Exam Syllabus Topics:

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

      SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

      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


      2. What properties can be assigned when comparing models in SAS Visual Statistics?

      A) The variables to include in the model comparison
      B) The learning rate for gradient boosting models
      C) The color scheme for visualization
      D) The number of observations in the dataset


      3. Which model does not produce score code?

      A) Regression using interaction effects
      B) Decision Tree using interactive mode
      C) Decision Tree using the rapid growth option
      D) Regression using the group by option


      4. You would like to see the minimum and maximum values for all of your measures so that you can filter variables as needed.
      Which is the most efficient way to do that?

      A) Create aggregated measures using the Min and Max aggregations.
      B) Select a histogram object for each measure.
      C) Select View Measure Details within the Actions menu to the right of the dataset name.
      D) Create a calculated item subtracting the Min aggregation from the Max aggregation.


      5. How is the KS Statistic typically used when interpreting an ROC chart?

      A) To identify the optimal prediction cut-off threshold
      B) To measure the area under the ROC curve
      C) To evaluate the overall performance of a classification model
      D) To assess the balance between sensitivity and specificity


      Solutions:

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

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