1z0-1096-23 Questions and Answers

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.

• Oracle Analytics Cloud and Oracle Autonomous Database are two Oracle Cloud services that are integrated with Oracle Machine Learning12.
• Oracle Analytics Cloud is a comprehensive cloud service that provides a unified platform for data preparation, analysis, visualization, and machine learning. It supports Oracle Machine Learning Notebooks, which enable data scientists and other data professionals to work collaboratively using SQL, PL/SQL, Python, and R1.
• Oracle Autonomous Database is a self-driving, self-securing, and self-repairing cloud service that supports various workloads such as data warehousing, transaction processing, and JSON document storage. It includes Oracle Machine Learning Services, which enable users to build, deploy, and manage machine learning models using REST APIs2.

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

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

https://oracle-base.com/articles/misc/sqlcl-format-query-results-with-the-set-sqlformat-command#loader

• The three Oracle Machine Learning (OML) components that are available with Oracle Autonomous Database are OML Notebooks, OML4SQL, and OML Services2.
• OML Notebooks are web-based interfaces for data analysis, data discovery, data visualization, and collaboration based on Apache Zeppelin notebook technology. They support SQL, PL/SQL, Python, R, and markdown interpreters on Oracle Autonomous Database2.
• OML4SQL is a set of in-database machine learning algorithms and SQL functions that enable data exploration, data preparation, model building, evaluation, and deployment using SQL and PL/SQL on Oracle Autonomous Database2.
• OML Services are RESTful web services that enable users to build, deploy, and manage machine learning models using REST APIs on Oracle Autonomous Database. They support native in-database models and ONNX-format models for classification, regression, and clustering2

Explanation

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

Explanation

https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/administer-oracle-machine-learning.html#GUID-E74F0E2E-EEE5-4421-A0BB-96A58811C04A Create User: An administrator creates a new user account and user credentials for Oracle Machine Learning in the User Management interface. Note: You must have the administrator role to access the Oracle Machine Learning User Management interface. To create a user account: Select an Autonomous Da-ta Warehouse instance and on the details page click Service Console. On the Service Console click Administration. Click Manage OML Users to open the Oracle Machine Learning User Administration page. Click Create on the Oracle Machine Learning User Administration page. In the Username field, enter a username for the account. Using the username, the user will log in to an Oracle Ma-chine Learning instance. Enter a name in the First Name field. Enter a name in the Last Name field. In the Email Address field, enter the email ID of the user. Select the option Generate password and email account details to user. User will be required to reset the password on first sign in. to auto generate a temporary password and send an email with the account credentials to the user. If you select this option, you need not enter values in the Password and Confirm Password fields; the fields are grayed out. In the Password field, enter a password for the user, if you choose to create a password for the user.This option is disabled if you select the Generate password... option to auto generate a temporary password for the user. In the Confirm Password field, enter a password to confirm the value that you entered in the Password field.By doing so, you create the password for the user. The user can change the password when first logging in. Click Create. This creates a new database user and grants the required privileges to use Oracle Machine Learning.

Explanation

Oracle Machine Learning Notebooks Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Note-books support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. View interactive product tour Oracle Machine Learning Notebooks with Autonomous Data Warehouse documentation Oracle Machine Learning Notebooks with Autonomous Transaction Processing documentation Oracle Ma-chine Learning Notebooks with Autonomous JSON Database documentation

• 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.

• 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:

• 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

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

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

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