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

• 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

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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.

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OML4Py Attribute Importance: Use this template notebook to identify key attributes that have maximum influence over the target attribute. The target attribute in the build data of a supervised model is the attribute that you want to predict. The template builds an Attribute Importance model using the SH schema data. learning/oml4py/1/mlpar/ Attribute Importance class oml.ai(model_name=None, model_owner=None, **params) In-database Attribute Importance Model Computes the relative importance of variables (aka attributes or columns) when predicting a target variable (numeric or categorical column). This function exposes the corresponding Oracle Ad-vanced Analytics in-database algorithm. Oracle Advanced Analytics does not support the predic-tion functions for attribute importance. The results of attribute importance are the attributes of the build data ranked according to their predictive influence. The ranking and the measure of im-portance can be used for selecting attributes. Attributes importance : oml.DataFrame Relative im-portance of predictor variables for predicting a response variable. It includes the following compo-nents: • variable: The name of the predictor variable • importance: The importance of the predictor variable • rank: The predictor variable rank based on the importance value.

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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.

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• To share a notebook with a colleague to run on a different Autonomous Database instance, you should export the notebook in .json format and have your colleague import this notebook4.
• Exporting a notebook in .json format preserves the content and format of the notebook, including the code cells, text cells, images, charts, and graphs. You can export a notebook from the Notebook menu or from the Notebooks page4.
• Importing a notebook in .json format allows you to upload an existing notebook file to your workspace and run it on your Autonomous Database instance. You can import a notebook from the Notebooks page or from the Create Notebook dialog box4.

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• 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:

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.

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%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.

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• A false statement about OML Notebooks is that an existing database user cannot be added as an OML user using the User Management Interface2.
• An existing database user can be added as an OML user using the User Management Interface by entering the existing database username and password in the Create User dialog box. This will enable the existing database user to access OML Notebooks without creating a new schema2.

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• Unsupervised machine learning is a type of machine learning in which algorithms learn patterns exclusively from unlabeled data34. Unsupervised learning algorithms discover hidden structures or groupings in the data without any supervision or guidance from human experts34.
• Three statements that are true about unsupervised machine learning are:

• 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.

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