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

Explanation

You can also collaborate by exporting your notebook as a JSON or JavaScript Object Notation file. This exported file can be imported into the same or different environment. To export a notebook as a JSON file, open the notebook in the notebook editor, click on the Export icon. A Save As dialog will open where you can specify the name of the JSON file and location on your system.

• The correct sequence of creating items in Oracle Machine Learning Notebooks when setting up a new Autonomous Database instance is Workspace, OML User, Notebook, Jobs1.
• A workspace is a logical container for organizing and managing notebooks, jobs, and projects. A workspace can be shared by multiple users with different roles and permissions1.
• An OML user is a database user who has access to Oracle Machine Learning Notebooks. An administrator needs to create an OML username and password for each user in the Oracle Machine Learning User Management interface2.
• A notebook is a document that contains SQL, PL/SQL, Python, or R code, as well as text, images, charts, and graphs. A notebook can be used for data exploration, data visualization, data preparation, and machine learning3.
• A job is a scheduled execution of a notebook or a script. A job can run on a recurring schedule or on demand. A job can also send notifications to users via email or webhooks4.

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

• 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.