Note: There are 5 correct answers to this question.

• A. granted access to read and write (create table)
• B. the model has been computed while at least one physical key variable was defined in the application,
• C. the in-database apply mode is not deactivated,
• D. the apply-in dataset (able, view, select statement, data manipulation) and the results dataset are tables coming from the same database,
• E. A web server such as Apache Web Server or Windows Internet Information Services (IIS).
• F. no error has occurred,

The Model Autosave panel allows you to activate the option that will automatically save the model at the end of the generation process and to set the parameters needed when saving the model. To activate the option, proceed as follows:
Note: There are 3 correct answers to this question.

• A. Be granted the SELECT SQL Privileges.
• B. Check Enable Model Autosave.
• C. Set the parameters listed in the following table.
• D. In the Summary of Modeling Parameters panel, click the Autosave
  button. The Model Autosave panel is displayed.

To Validate the Model Generated :
Note: There are 2 correct answers to this question.

• A. Verify the Predictive Power Automated Analytics User Guides and Scenarios Modeler If the performance of the model meets your requirements, go to Step 3 - Analyzing and Understanding the Model Generated. Otherwise, go to the procedure To Generate a New Model.
• B. A web server such as Apache Web Server or Windows Internet Information Services (IIS).
• C. You can also check other indicators provided in addition to KI and KR during the model generation. For example, you could view the total elapsed time required to generate the model and information on the standard error rate.

The term model carries many different meanings depending on its field of application. In data mining, a model describes and explains the relationships that exist between input data (one or more target variablest allows one to predict and explain phenomena, or to describe them. To quote George E.P. Box,
"All Models Are Wrong But Some Are Useful" from "Robustness is the Strategy of Scientific Model Building" in Robustness in Statistics. Eds., R.L. Launer and G.N. Wilkinson, 1979, Academic Press. A model that has satisfactory performance is one that possesses both:
Note: There are 2 correct answers to this question.

• A. High robustness, that is, sufficient capacity to repeat the same performance on new datasets containing observations of a similar nature to the training dataset. This explanatory power is indicated by the prediction confidence .
• B. Both of these ports must be accessible from the client machine. For example, network firewalls should allow communication on these ports between client machine and the servers.
• C. High explanatory power, that is, sufficient capacity to explain the target variable. This explanatory power is indicated by the predictive power of the model.