Free C_PAII10_35 – SAP Certified Application Associate - SAP Predictive Analytics Practice Test Question

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  Variable Contributions, that is, relative importance of each variable in the built model.
  

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  Variable Weights, that is, weights Guides and Scenarios Modeler P U B L I C 97
  

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  Smart Variable Contributions, that is, the variables internal contributions. Automated Analytics User Guides and Scenarios Modeler P U B L I C 97
  

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  Maximum Smart Variable Contributions, that is, the maximum smart variable contributions including only the maximum of similar variables. For example, only binned encoding of the continuous variable age will be displayed. This is the chart displayed by default.
  

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  Perform various analyses and build models on the data, including time series forecasting, outlier detection, trend analysis, classification analysis, segmentation analysis, and affinity analysis.
  
  
  

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  Predictive and explanatory models, which allow one to predict and explain phenomena,

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  The SAP Predictive Analytics server must be able to access the name server.

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  The client application must be able to access both the name server and the SAP Predictive Analytics server.

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  Descriptive models, which allow one to describe datasets.
  
  
  

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  In the Summary of Modeling Parameters panel, click the Autosave button. The Model Autosave panel is displayed.

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  Check Enable Model Autosave.
  

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  Be granted the SELECT SQL Privileges.
  

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  Set the parameters listed in the following table.
  
  
  

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  a decision tree,
  

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  a neural network,
  

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  a Certification Authority certificate (CA certificate)
  

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  a mathematical function.
  
  
  

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  Text splitting
  

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  Stemming reduction
  

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  Language recognition
  

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  Synonym replacement
  
  
  

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  m = sqrt(M) for regression and M/3 for classification
  

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  m = M/3 for regression and M/3 for classification
  

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  m = sqrt(M) for regression and sqrt (M) for classification
  

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  m = M/3 for regression and sqrt (m) for classification
  
  
  

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  Inner join
  

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  Right outer join
  

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  Cross join (Cartesian product)
  

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  Left outer join
  
  
  

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  Generate Source Code
  

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  Apply Model
  

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  Save Model
  

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  Load a Model
  
  
  

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  Data from one component from one analysis
  

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  Data from one component from all analyses
  

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  Data from all components from all analyses
  

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  Data from all components from one analysis
  
  
  

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  The cluster target means
  

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  The overall model Ki
  

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  The cluster frequencies
  

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  The winning number of clustersc