Oracle 1Z0-1096-23 Exam Questions

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/use-library-collaborate-users.html#GUID-F468F691-2B8B-4427-B517-5F149AA57239 Use Templates to Collaborate with Users In the Oracle Machine Learning Templates UI, you can collaborate with other users by sharing your work, publishing your work as reports, and by creating notebooks from tem-plates. You can store your notebooks as templates, share notebooks, and provide sample templates to other users. Note: You can also collaborate with other Oracle Machine Learning Notebook users by providing access to your workspace. The authenticated user can then access the projects in your workspace, and access your notebooks. The access level depends on the permission type granted - Manager, Developer, or Viewer. For more information about collaboration among users, see unre-solvable-reference.html#GUID-7079D42F-9308-4BEF-B2F2-E74E49A22A87 Use the Personal Templates Personal Templates lists the notebook templates that you have created. Use the Shared Templates In the Shared Templates, you can share notebook templates with all authenticated users the notebook templates you create from existing notebooks available in Templates. Use the Example Templates The Example Templates page lists the pre-populated Oracle Machine Learning notebook templates. You can view and use these templates to create your notebooks.

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/administer-oracle-machine-learning.html#GUID-E74F0E2E-EEE5-4421-A0BB-96A58811C04A Create User: An administrator creates a new user account and user credentials for Oracle Machine Learning in the User Management interface. Note: You must have the administrator role to access the Oracle Machine Learning User Management interface. To create a user account: Select an Autonomous Da-ta Warehouse instance and on the details page click Service Console. On the Service Console click Administration. Click Manage OML Users to open the Oracle Machine Learning User Administration page. Click Create on the Oracle Machine Learning User Administration page. In the Username field, enter a username for the account. Using the username, the user will log in to an Oracle Ma-chine Learning instance. Enter a name in the First Name field. Enter a name in the Last Name field. In the Email Address field, enter the email ID of the user. Select the option Generate password and email account details to user. User will be required to reset the password on first sign in. to auto generate a temporary password and send an email with the account credentials to the user. If you select this option, you need not enter values in the Password and Confirm Password fields; the fields are grayed out. In the Password field, enter a password for the user, if you choose to create a password for the user.This option is disabled if you select the Generate password... option to auto generate a temporary password for the user. In the Confirm Password field, enter a password to confirm the value that you entered in the Password field.By doing so, you create the password for the user. The user can change the password when first logging in. Click Create. This creates a new database user and grants the required privileges to use Oracle Machine Learning.

To segment your customer data for marketing research purposes and identify homogeneous groups to build supervised models, you should use clustering12.

Clustering is a type of unsupervised machine learning that groups data points based on their similarities in terms of features or attributes.Clustering can help discover the underlying structure of the data and reveal the natural segments or categories within it12.

Clustering can be useful for marketing research because it can help identify different types of customers based on their demographics, preferences, behaviors, or needs.Clustering can also help create customer profiles or personas that can be used to target specific segments with tailored marketing campaigns or offers12.

Clustering can also be used as a preliminary step for building supervised models, such as classification or regression.By using the cluster labels as an additional feature or a target variable, supervised models can learn from the cluster information and improve their accuracy or performance12.

https://www.doag.org/formes/pubfiles/13151859/OE-DAC-Oracle-Machine-Learning-Overview-Whats-New-Customers-Apps_MLconcepts_Berger.pdf

When managing machine learning models using the OML AutoML UI, three actions that can be performed on Oracle Machine Learning (OML) models from within OML AutoML UI are:

Create a notebook with auto-generated OML4Py code from an OML in-database model to allow for further model tweaking and batch scoring.This feature enables users to export the selected model as a notebook that contains the OML4Py code to reproduce the model building process and perform additional tasks such as model evaluation, scoring, or deployment1.

Deploy an OML in-database model to a REST endpoint.This feature enables users to deploy the selected model as a RESTful web service that can be accessed by external applications or tools for real-time scoring or predictions2.

Review the model statistics associated with the experiment of an OML in-database model.This feature enables users to view the details of the model such as the algorithm name, hyperparameters, performance metrics, feature importance, and confusion matrix3.