Oracle Updated 1z0-1096-23 Exam Questions and Answers by tudor

Explanation

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.

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/grant-workspace-permissions.html https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/workspace-permissions.html

Explanation

https://www.oracle.com/a/tech/docs/otn-batch1/oml-automl-ui-tech-brief.pdf https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/create-experiment.html

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/process-overview.html#GUID-A630712B-C18C-4765-923F-6C863898D208 Process Overview: The lifecycle of a machine learning project is divided into six phases. The process begins by defining a business problem and restating the business problem in terms of a machine learning objective. The end goal of a machine learning process is to produce accurate results for solving your business problem. Workflow: The machine learning process workflow illustration is based on the CRISP-DM method-ology. Each stage in the workflow is illustrated with points that summarize the key tasks. The CRISP-DM methodology is the most commonly used methodology for machine learning. The following are the phases of the machine learning process: Define business goals Understand data Pre-pare data Develop models Evaluate Deploy