Free SAP Certified Application Associate – SAP Predictive Analytics C_PAII10_35 Exam Practice Test

A model with a predictive power of:Note: There are 3 correct answers to this question.

• '0.79' is capable of explaining 79% of the information contained in the target variable using the explanatory variables contained in the dataset analyzed.
• '1' is a hypothetical perfect model, capable of explaining 100% of the target variable using the explanatory variables contained in the dataset analyzed. In practice, such a predictive power would generally indicate that an explanatory variable 100% correlated with the target variable was notexcluded from the dataset analyzed.
• '0' is a purely random model
• The system account of the authenticated user, this is the default with the system authentication

To generate a time series model, you must select a partition strategyto cut your training dataset into the three sub-sets: training, validationand testing. Since the order of the observations in the dataset isimportant for the modeling, only two types of partition strategies areavailable in time series:Note: There are 2 correct answers to this question.

• Sequential with Testing
• Sequential without Testing
• In the IIS Administrative Tools pane, click Internet Information Services (IIS) Manager.

Once your business issue has been identified and formulated, you need to have data on hand that will permitan answer to be found. In order for your data to be usable by the application, the following five conditions must be met:Note: There are 5 correct answers to this question.

• You must have a sufficiently large volume of data to be able to build a valid model, that is, in order for the model to be both relevant and robust. An analytical model that is generated from a dataset of 50 lines may have low generalization capacity, and contain low informative value. We can advise you on the issues of data volume.
• Your dataset must contain a target variable, that will allow you to express your business issue within the application.
• The target variable must be known for each observation of the training dataset. To express this another way, no target variable values may be missing over the range of the entire training dataset.
• The data source format must be supported by the application.
• Your data must be presented in the form of a single table of data , except in instances where you are using Event Logging or Sequence Coding features of Data Manager.
• Server instances are going to be started with a different system account than the authenticated server.

The variable 'product purchased' is your target variable:it corresponds to your business issue. It is:Note: There are 2 correct answers to this question.

• Known for all values of the training dataset.
• Not known for the values of the application dataset.
• knows the main Linux administration commands (managing users, groups, and so on),
• knows how to set up ODBC drivers on Linux systems.

How are missing values handled in AutomatedAnalytics?Note: There are 1 correct answers to this question.

• They are automatically ignored.
• They are assigned to a category.
• They are automatically filled.
• They are considered as outliers.

What type of join is generated when creatingAnalytical Data Set and Data Manipulation from theData Manager?Note: There are 1 correct answers to this question.

• Inner join
• Right outer join
• Cross join (Cartesian product)
• Left outer join

Detected profit is the default setting for type of profit. Using this type of profit:Note: There are 2 correct answers to this question.

• The value '0' is assigned to observations that do not belong to the target category of the target variable,
• The value '1/(frequency of the target variable in the dataset) is assigned to observations that do belong to the target category of the target variable.
• Modeler enables the analyst to create in a homogenous and easy-handling workflow models such as classification, regression, clustering, time series, and association rules. Models can be exported in different formats so that you can easily apply them in your production environment.
• Data Manager is used to facilitate the preparation of the data to be used in the analytics project.

A user has created several analyses with a chain in thePredict room. What data can the user leverage in avisualization in the Visualize room when these analysesare executed successfully?Note: There are 1 correct answers to this question.

• Data from one component from one analysis
• Data from one component from all analyses
• Data from all components from all analyses
• Data from all components from one analysis

In Model Manager with default configuration,which access privileges provide the 'server usage'statistics?Note: There are 2 correct answers to this question.

• IT Administrator
• Server Owner
• Business Owner
• IT Supervisor

Operation of Automated Analytic:Note: There are 3 correct answers to this question.

• Automated Analytics allows you to perform supervised data mining, that is, to transform your data into knowledge, then into action, as a function of a domain-specific business issue.
• The application supports various formats of source data order to be usable by the application features, the dataset to be analyzed must be presented in the form of a single table of data, except in instances where you are using the Event Logging or Sequence Coding features of Data Manager.
• To use the application's features, you must have a training dataset available that contains the target variable with all its values defined. Then, you can apply the model generated using the training dataset to one or more application datasets.
• The Predictive Factory server contains a group of services handled by the Server Intelligence Agent (SIA). The services can be stopped and started with the Start and Stop Predictive Factory commands