Oracle 1z0-1096-23 Dumps

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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.

• 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

• 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:

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• View notebooks. An Administrator can view notebooks in their own workspace or in workspaces where they have collaboration rights. However, an Administrator cannot run or modify notebooks1.
• Reassign workspaces to users. An Administrator can reassign workspaces from one user to another user in the User Data page. This can be useful when a user leaves the organization or changes roles2.
• Create, edit and delete OML users. An Administrator can create new OML user accounts and passwords, edit existing OML user information, and delete OML users in the User Management interface.

Explanation

• The three types of permissions that can be granted to a user to collaborate and access a workspace in Oracle Machine Learning Notebooks are Viewer, Manager, and Developer1.
• Viewer is a user role that allows users to view notebooks but not run or modify them1.
• 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.

• 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.

• The step that is not required to be performed by an administrator when adding a new user to Oracle Machine Learning Notebooks is providing the user with Autonomous Database client wallet for remote credentials.
• The client wallet is only needed for remote access to the database using tools such as SQL Developer or Python. For accessing Oracle Machine Learning Notebooks, the user only needs an OML username and password, which are created by the administrator in the Oracle Machine Learning User Management interface

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• 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

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Process Overview: The lifecycle of a machine learning project is divided into six phases. The process begins by defining a business problem and restating the business problem in terms of a machine learning objective. The end goal of a machine learning process is to produce accurate results for solving your business problem. Workflow: The machine learning process workflow illustration is based on the CRISP-DM method-ology. Each stage in the workflow is illustrated with points that summarize the key tasks. The CRISP-DM methodology is the most commonly used methodology for machine learning. The following are the phases of the machine learning process: Define business goals Understand data Pre-pare data Develop models Evaluate Deploy

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• 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.

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• 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.

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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.

• Classification algorithms are supervised learning methods that assign cases to target categories based on a set of input features12. For example, a classification algorithm can predict whether an email is spam or not based on its content and sender.
• Classification algorithms require known outcomes to guide the learning process, which means they need labeled data for training and evaluation12. For example, a classification algorithm can learn from a set of emails that are already labeled as spam or not by humans

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