1z0-1096-23 Exam Dumps - Oracle Cloud Infrastructure Certification Questions and Answers

Explanation

https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/create-notebooks-oml.html#GUID-0F489564-A628-478E-9898-1F94B0441026

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/administer-oracle-machine-learning.html#GUID-E74F0E2E-EEE5-4421-A0BB-96A58811C04A

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/use-scratchpad.html

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.

Explanation

The three data sources that can be immediately used for model building in Oracle Machine Learning are: Oracle Database tables: Oracle Machine Learning can directly access and use data from tables within an Oracle Database for model building. This allows for efficient utilization of the database's structured data for machine learning tasks. Oracle Database views: Similar to tables, Oracle Machine Learning can also utilize data from database views. Views provide a virtual representation of the data, allowing users to access and analyze specific subsets or transformations of the underlying tables. CSV files: Oracle Machine Learning supports the use of CSV (Comma-Separated Values) files as a data source for model building. CSV files provide a simple and widely-used format for storing tabular data, making it easy to import and use within Oracle Machine Learning. Therefore, the correct options are: Oracle Database tables Oracle Database views CSV files

https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/omlug/output-formats-supported-set-sqlformat.html

Explanation

Grant Workspace Permissions You can collaborate with other users in Oracle Machine Learning by granting permissions to access your workspace. Your workspace contains your projects and note-books. By granting different types of permissions such as Manager, Developer, and Viewer, you can allow another user to view your workspace and perform different tasks in your projects and note-books such as edit, create, update, delete, run, view notebooks and so on. For more information about the permission types, see About Workspace Permission Types. Caution: If you grant the per-mission type Manager or Developer, then the user can also drop tables, create tables, and run any scripts at any time on your account. The user with Viewer permission type can only view your note-books, and is not authorized to run or make any changes to your notebooks

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/1/mlpug/about-machine-learning-algorithms.html#GUID-00F8AF8F-6652-4161-BEEF-E737A68FB53C

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/output-formats-supported-set-sqlformat.html