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Certification A00-485 Training | Premium A00-485 Files

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Posted on: 01/15/25

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SASInstitute Modeling Using SAS Visual Statistics Sample Questions (Q53-Q58):

NEW QUESTION # 53
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 a calculated item subtracting the Min aggregation from the Max aggregation.
  • B. Select a histogram object for each measure.
  • C. Create aggregated measures using the Min and Max aggregations.
  • D. Select View Measure Details within the Actions menu to the right of the dataset name.

Answer: D


NEW QUESTION # 54
Refer to the exhibits:
Exhibit 1:

Exhibit 2:

An analyst has created a cluster model based on the settings in Exhibit 1 and chose to derive a cluster ID variable.
Why are there 5 cluster IDs in Exhibit 2 based on these settings?

  • A. Parallel Coordinates Visible Roles setting is 5.
  • B. There are observations with missing values.
  • C. Variable Standardization has been chosen.
  • D. Cluster Matrix Visible Roles setting is 5.

Answer: B


NEW QUESTION # 55
How can you interpret model results using the advanced group-by feature in SAS Visual Analytics?

  • A. Visualize the predicted values for each level of the group-by variable
  • B. Analyze the impact of different levels of the group-by variable on the model outcome
  • C. Explore interaction effects between the group-by variable and other predictors
  • D. Review the model coefficients for each level of the group-by variable

Answer: A,B,C,D


NEW QUESTION # 56
During cluster creation, if the Variable Standardization option is selected, where in SAS Visual Statistics can you view the standardization formula?

  • A. cluster summary table
  • B. score code
  • C. cluster matrix plot
  • D. parallel coordinates plot

Answer: B


NEW QUESTION # 57
How can you interpret the impurity measures in an icicle plot of a decision tree?

  • A. Impurity measures are irrelevant in decision tree analysis.
  • B. Lower impurity implies more homogeneity within a leaf node.
  • C. Higher impurity indicates better separation of classes.
  • D. Impurity measures represent the number of data points in a leaf node.

Answer: B


NEW QUESTION # 58
......

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