How can you refine your model in the Select Variables tool in Automated Analytics? Note: There are 2 correct answers to this question.

A. Select the model iteration.

B. Analyze variable deviations.

C. Simulate the model application.

D. Display variable correlations.

The option Analyze Deviations is a tool that provides you with a diagnostic of the data statistical variation. This option can be used for several purposes:

Note: There are 3 correct answers to this question.

A. to compare the distribution of a new dataset with the distribution of the dataset used to train the model,

B. to check the quality of new data after loading them,

C. to check if your data have evolved over time and thus if the model need to be adapted to the new data.

D. The Root entries are used for tracing the activity of the server which can later be used for debugging purposes.

A polynomial may be of degree 1, 2, 3 or greater. By defining the polynomial degree, you are defining the degree of complexity of the model. Examples of Polynomials:

Note: There are 2 correct answers to this question.

A. A polynomial of degree 1 is of the form: f

B. The server private key

C. The server certificate

D. Xn A polynomial of degree 2 is of the form: f

Obtaining a better model is achieved by:

Note: There are 3 correct answers to this question.

A. Improving the prediction confidence of the model

B. Improving the predictive power of the model

C. System authentication is to be used through Pluggable Authentication Module (PAM). Access to Linux System account password required root privileges.

D. Improving both the predictive power and he prediction confidence of the model.

Once the modeling parameters are defined, you can generate the model. Then you must validate its performance using the predictive power.

Note: There are 2 correct answers to this question.

A. If the model is sufficiently powerful, you can analyze the responses that it provides in relation to your business issue apply it to new datasets

B. Otherwise, you can modify the modeling parameters in such a way that they are better suited to your dataset and your business issue, and then generate new, more powerful models.

C. The Progress Bar displays the progression for each step of the process. It is the screen displayed by default.

D. The Detailed Log displays the details of each step of the process.

The Decision Tree Each node in the tree displays: Note: There are 4 correct answers to this question.

A. The name of the expanded variable, for example Marital-status.

B. The categories on which the node population has been filtered, for example {Married-AFspouse;Nevermarried}

C. The Population of the node

D. The ratio of Positive Target (for nominal targets) or the Target Mean (for continuous targets)

E. The Usage entries are sparser and report a more global view of the activity on the server. They consist of one line per event on the following events: user connection, user disconnection, training a model, applying a model, saving a model.

What is the first phase of the CRISP- DM predictive modeling process? Note: There are 1 correct answers to this question.

A. Model building

B. Business understanding

C. Data understanding

D. Data preparation

The importance of a category depends on both its difference to the target category mean and the number of represented cases. High importance can result from any of the following:

Note: There are 3 correct answers to this question.

A. A high discrepancy between the category and the mean of the target category of the target variable

B. A minor discrepancy combined with a large number of records in the category

C. A combination of both

D. recise scheduling of main industrialization tasks

Depending on the type of the target, the model graph plot allows you to:

Note: There are 3 correct answers to this question.

A. View the realizable profit that pertains to your business issue using the model generated when the target is nominal.

B. Compare the performance of the model generated with that of a random type model and that of a hypothetical perfect model when the target is nominal.

C. A web server such as Apache Web Server or Windows Internet Information Services (IIS).

D. Compare the predicted value to the actual value when the target is continuous .

Operation of Automated Analytic:

Note: There are 3 correct answers to this question.

A. 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.

B. 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.

C. 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.

D. 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