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

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-automl-ui/amlui/create-experiment.html

Explanation

https://learn.oracle.com/ols/course/using-oracle-machine-learning-with-autonomous-database/35644/98086/149312 This is from Oracle Course : Using Oracle Machine Learning with Autonomous Database Regression is a supervised learning technique used when the output attribute is a continuous numerical value, for example, the price of a property based on the attributes of the property and its locality, the humidity of an area, and so on. Here is a list of all the algorithms offered by Oracle Machine Learning. Unsupervised learning does not involve direct control from the developer. In terms of defining an output attribute, the output attribute is a dependent attribute. And as there is no output attribute in unsupervised learning, there is no distinction between dependent and independent attributes.

Explanation

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

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.

• Contributor is not a type of workspace permission that can be granted to other users in Oracle Machine Learning Notebooks1.
• The types of workspace permissions that can be granted to other users are Manager, Developer, and Viewer1.
• Manager is a user role that allows users to perform all the tasks of a Developer as well as manage other users’ workspaces and permissions1.
• Developer is a user role that allows users to create and run notebooks, run SQL statements, create SQL scripts, run Python scripts, create jobs to schedule and run notebooks, use example template notebooks, create and run AutoML experiments, and deploy models1.
• Viewer is a user role that allows users to view notebooks but not run or modify them1

Explanation

https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/autonomous-oml.html#GUID-63F2D68B-6D44-48A0-AE51-C6AAE4635212

• 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/oml4py/1/mlpug/about-machine-learning-algorithms.html#GUID-00F8AF8F-6652-4161-BEEF-E737A68FB53C

Explanation

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

• 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

https://docs.oracle.com/en/cloud/paas/autonomous-database/oml-tour/

Explanation

https://blogs.oracle.com/machinelearning/post/introducing-oml-automl-user-interface https://www.oracle.com/a/tech/docs/technical-resources/oml-technical-brief.pdf

• 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

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/create-check-box-forms.html https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/create-select-forms.html https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/create-text-input-forms.html

Explanation

Unsupervised machine learning uses a more independent approach, in which a computer learns to identify complex processes and patterns without a human providing close, constant guidance. Un-supervised machine learning involves training based on data that does not have labels or a specific, defined output. To continue the childhood teaching analogy, unsupervised machine learning is akin to a child learning to identify fruit by observing colors and patterns, rather than memorizing the names with a teacher's help. The child would look for similarities between images and separate them into groups, assigning each group its own new label. Examples of unsupervised machine learning algorithms include k-means clustering, principal and independent component analysis, and association rules.

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

Explanation

About Workspace Permission Types: Oracle Machine Learning allows three types of permissions. Depending on the permission type, you can allow the user to view or perform different tasks in your workspace, projects, and notebooks. The three types of permissions are listed in the following table along with the actions that are allowed. Permission Types || Actions based on permission > Manager: • Project: Create, update, delete. • Workspace: View only. • Notebooks: Create, update, run, delete, and schedule jobs. > Developer: • Project: View only. • Workspace: View only. • Notebooks: Create, update, run, and delete notebooks that a developer creates only. • Jobs: View and run jobs of shared notebooks only. A developer cannot create jobs for notebooks that are shared. > Viewer: • Project: View only. • Workspace: View only. • Notebooks: View only. • Jobs: View jobs and job runs of shared notebooks only.

Explanation

https://docs.oracle.com/cd/F32587_01/dmcon/oracle-machine-learning-sql-concepts.pdf

Explanation

https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/autonomous-sql-commands.html

• The data preparation step for analyzing data with Oracle Machine Learning involves various actions to transform the raw data into a suitable format for machine learning algorithms45.
• Some of the actions that would typically be performed during the data preparation step are:

Explanation

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