1z0-1096-23 Exam Dumps - Oracle Machine Learning using Autonomous Database 2023 Associate

Explanation

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

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.

• 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.

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/administer-oracle-machine-learning.html#GUID-E74F0E2E-EEE5-4421-A0BB-96A58811C04A

• Running this command will return the median value of customer credit limit grouped by marital status2.
• The MEDIAN function is an aggregate function that returns the middle value of a numeric column for each group or partition. If the number of values is even, it returns the average of the two middle values2.
• The GROUP BY clause is used to divide the rows of a table into groups that have the same values in the specified columns. The aggregate functions are applied to each group separately and the result is returned for each group3.

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/oracle-machine-learning-sql-guide.pdf

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-jobs.html#GUID-DAE3D097-2EB5-4345-88F5-B4780EFD634A

• Oracle Machine Learning algorithms are suitable for various use cases that involve data analysis, prediction, classification, clustering, association, and feature extraction56.
• Three use cases that are suitable for Oracle Machine Learning algorithms are:

Explanation

Oracle Machine Learning Notebooks is an Apache Zeppelin-based collaborative web-based inter-face that provides a development environment to create machine learning notebooks where you can perform data exploration and visualizations, data preparation and machine learning. https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-oracle-machine-learning.html

Explanation

About Data Loading: Autonomous Database provides the following loading options: You can load data using Oracle Database Actions. You can load data using Oracle Database tools and Oracle or other 3rd party data integration tools. On transaction processing systems you traditionally ingest data through routine transactions or with DML operations. In general you load data from files local to your client computer or from files stored in a cloud-based object store. To load data from files in the cloud, use either Oracle Database Actions or use the Autonomous Database PL/SQL package DBMS_CLOUD to load files from the cloud. For the fastest data loading experience Oracle recommends uploading the source files to a cloud-based object store, such as Oracle Cloud Infrastructure Object Storage, before loading the data into your database. Oracle provides support for loading files that are located locally in your data center, but when using this method of data loading you should factor in the transmission speeds across the Internet which may be significantly slower. For more information on Oracle Cloud Infrastructure Object Storage, see Putting Data into Object Storage and Overview of Object Storage. Note: If you are not using ADMIN user, ensure the user has the necessary privileges for the operations the user needs to perform. See Manage User Privileges on Autonomous Database - Connecting with a Client Tool for more information.

Explanation

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

• The reason why a user with Developer permission is unable to create a job on an existing notebook that is shared is that the user requires the Create Job role1.
• The Create Job role is a database role that grants the privilege to create and manage jobs on Oracle Machine Learning Notebooks. This role is not granted by default to any user, including the ADMIN user. An administrator needs to explicitly grant this role to users who need to create jobs1

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/oracle-machine-learning-sql-guide.pdf

Explanation

https://apexapps.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?wid=891 &clear=180&session=109742845155042

• The first step for analyzing data that is external to Oracle Database with Oracle Machine Learning is to load the data34.
• Loading the data means transferring the external data into Oracle Database tables or views, which can then be accessed by Oracle Machine Learning tools and functions. Loading the data can be done by using various methods, such as SQL*Loader, Data Pump, External Tables, or Oracle Database Gateway34.
• Loading the data is necessary because Oracle Machine Learning operates on data that is stored inside Oracle Database. By loading the external data into Oracle Database, users can leverage the benefits of in-database analytics, such as scalability, performance, security, and integration34.

• A false statement regarding Oracle Machine Learning (OML) is that OML offerings need a separate data visualization tool for creating visualization56.
• OML does not need a separate data visualization tool for creating visualization because it provides various options for visualizing data and models within its offerings. For example, OML Notebooks support interactive charts and graphs using Plotly and Matplotlib libraries for Python and R. OML SQL also supports native SQL functions for creating histograms, scatter plots, box plots, and more

Explanation

%sql - Allows you to run SQL statements. Use the Scratchpad The Scratchpad provides you convenient one-click access to a notebook for running SQL statements, PL/SQL scripts, and Python scripts that can be renamed. The Scratchpad is available on the Oracle Machine Learning Notebooks home page. Note: The Scratchpad is a regular notebook that is prepopulated with three paragraphs - %sql, %script, and %python. After you run your scripts, the Scratchpad is automatically saved as a notebook by the default name Scratchpad in the Notebooks page. You can access it later in the Notebooks page. You can run all the paragraphs together or one paragraph at a time. To open and use the scratchpad, click Scratchpad on the Oracle Machine Learning Notebooks home page under Quick Actions. The Scratchpad opens. The Scratchpad has three paragraphs each with the following directives:%sql - Allows you to run SQL statements.%script - Allows you to run PL/SQL scripts.%python - Allows you to run Python scripts.

Explanation

Jobs allow you to schedule the running of notebooks. In the Jobs page, you can create jobs, duplicate jobs, start and stop jobs, delete jobs, and monitor job status by viewing job logs, which are read-only notebooks. About Jobs. The Jobs page lists all the jobs created, along with the job name, notebook, owner of the job, last start date, next run date, status, and schedule. Create Jobs to Schedule Notebooks You can create jobs to schedule your notebook with preferred scheduling set-tings. View Job Logs You can view the historical logs of any particular job in the Job Log interface. About Jobs The Jobs page lists all the jobs created, along with the job name, notebook, owner of the job, last start date, next run date, status, and schedule.