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NEW QUESTION # 21
You have created a workspace in Oracle Machine Learning Notebooks and want to share it with collaborators by granting permissions to access your workspace. You want to enable other users to run and modify your notebooks but do not want to provide the ability to schedule jobs that run your notebooks. Which permission type should be granted to this user?
- A. Manager
- B. Designer
- C. Viewer
- D. Developer
Answer: D
Explanation:
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.
NEW QUESTION # 22
Which three Oracle Machine Learning (OML) Notebooks capabilities are enabled when an OML user is created with the oml_developer role? (Choose three.)
- A. Save scripts to the Python script repository.
- B. Create and execute OML Notebooks.
- C. Share notebooks with other OML users.
- D. Transfer OML workspace to other users.
Answer: A,B,C
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/administer-oracle-machine-le
NEW QUESTION # 23
You have created an Oracle Machine Learning notebook and want to share it with another collaborator.
However, you do not want to provide the ability to run or modify the notebook in your workspace. Which three options can be used to do this? (Choose three.)
- A. Provide the user Viewer permission to your workspace
- B. Share the notebook as a Shared Oracle Machine Learning Template
- C. Export the notebook and import it into the other user's project
- D. Provide the user Developer permission to your workspace.
Answer: A,B,C
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/collaborate-oracle-machine-l
NEW QUESTION # 24
Which two can be performed by an Administrator in Oracle Machine Learning Notebooks? (Choose two.)
- A. View notebooks in read-only mode
- B. Manage any notebook
- C. Reassign user workspace
- D. Run noteboooks
Answer: A,C
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/administer-oracle-machine-le
NEW QUESTION # 25
To navigate to a specific notebook provided by another user, which is the correct workflow?
- A. Change project > Change workspace > Open notebook
- B. Select workspace > Select project > List notebooks > Open notebook
- C. Create user > Change workspace > Open notebook
- D. Change user > Open notebook > Create project
Answer: B
Explanation:
Explanation
https://apexapps.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?wid=891&clear=180&session=109742845
NEW QUESTION # 26
Which four actions would typically be performed during the data preparation step for analyzing data with Oracle Machine Learning?
- A. missing value replacement
- B. building a machine learning model
- C. binning of numeric data
- D. performing feature engineering, such as creating derived variables
- E. numeric data normalization
- F. data collection from various sources
Answer: A,C,D,E
Explanation:
* 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:
* Numeric data normalization: This is a technique for reducing the range of numerical data by mapping them to a standard scale, such as 0 to 1. Normalization can improve the performance and stability of some machine learning algorithms5.
* Missing value replacement: This is a technique for handling missing or null values in the data, which can cause errors or bias in some machine learning algorithms. Missing values can be replaced by various methods, such as mean, median, mode, or a constant value4.
* Performing feature engineering, such as creating derived variables: This is a technique for creating new features from existing ones or combining them in meaningful ways. Feature engineering can enhance the predictive power and interpretability of machine learning models4.
* Binning of numeric data: This is a technique for reducing the cardinality of continuous and discrete data by grouping related values together in bins. Binning can improve resource utilization and model build response time without significant loss in model quality. Binning can also strengthen the relationship between attributes and improve model quality5
NEW QUESTION # 27
Which three types of forms are available in Oracle Machine Learning Notebooks? (Choose three.)
- A. Radio form
- B. List form
- C. Check Box form
- D. Select form
- E. Text Input form
Answer: C,D,E
Explanation:
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
NEW QUESTION # 28
In an Oracle Machine Learning notebook, which is the correct syntax for starting a SQL script in Scratchpad?
- A. "#sql"
- B. "%sql"
- C. "$sql"
- D. "@sql"
Answer: B
Explanation:
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.
NEW QUESTION # 29
Which three are unsupervised machine learning algorithms? (Choose three.)
- A. Principal Component Analysis
- B. Naive Bayes
- C. Random Forest
- D. K-means clustering
- E. Logistical Regression
- F. Association rule
Answer: A,D,F
Explanation:
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.
NEW QUESTION # 30
You are creating a job that should run a notebook every hour. You want to make sure that the job does not run repeatedly if there are more than five consecutive failures to run the job. Which option should you set while creating the job?
- A. Minimum Failure Allowed
- B. Timeout in Minutes
- C. Maximum Failure Allowed
- D. Maximum Number of Runs
Answer: C
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-jobs.html#GUID-
NEW QUESTION # 31
Which output formats are supported by the SET SQLFORMAT command? (Choose three.)
- A. JSON
- B. CSV
- C. TXT
- D. HTML
(Correct)
Answer: A,B
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/output-formats-supported-set
NEW QUESTION # 32
Which two templates are supported while saving a notebook? (Choose two.)
- A. Shared
- B. Public
- C. Personal
- D. Example
Answer: A,C
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks-templates.html
NEW QUESTION # 33
What is the correct sequence of creating items in Oracle Machine Learning (OML) Note-books when setting up a new Autonomous Database instance?
- A. OML User, Notebook, Job
- B. Notebook, Job, Project, OML User
- C. Workspace, OML User, Notebook, Jobs
- D. Job, Project, Workspace, Notebook
Answer: C
Explanation:
* 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.
NEW QUESTION # 34
Which two statements are true about supervised machine learning? (Choose two.)
- A. It generally results in predictive models.
- B. It is used to extract meaningful insights from raw data to improve data operational efficiency.
- C. There is no previously known result to guide the algorithm in building the model.
- D. It does not specify a target, it can be applied to a population of interest.
- E. The learning process is directed by a previously known dependent attribute or target.
Answer: A,E
Explanation:
Explanation
https://docs.oracle.com/cd/F32587_01/dmcon/oracle-machine-learning-sql-concepts.pdf
NEW QUESTION # 35
Which feature of Oracle Machine Learning Notebooks should you use for data analysis, da-ta discovery, data visualization, and collaboration on Oracle Autonomous Database?
- A. Workspace
- B. Job
- C. Notebook
- D. Project
Answer: C
Explanation:
Explanation
https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/create-notebooks-oml.html#GUID-0F489564
NEW QUESTION # 36
Which three statements are true about unsupervised machine learning? (Choose three.)
- A. It uses unlabeled data.
- B. It can be used as a preliminary step for supervised algorithms.
- C. There is no previously known result to guide the algorithm in building the model.
- D. It analyzes cases where the target value is already known.
Answer: A,B,C
Explanation:
* 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:
* There is no previously known result to guide the algorithm in building the model. Unsupervised learning algorithms do not have any predefined target variable or outcome to optimize. They rely on the intrinsic properties of the data to find meaningful patterns or clusters34.
* It can be used as a preliminary step for supervised algorithms. Unsupervised learning algorithms can be useful for exploratory data analysis, feature extraction, dimensionality reduction, or data preprocessing before applying supervised learning algorithms34.
* It uses unlabeled data. Unsupervised learning algorithms do not require any labeled data or annotations to learn from. They can work with raw or unstructured data such as text, images, audio, or video34.
NEW QUESTION # 37
In which format is an Oracle Machine Learning Notebooks notebook saved or exported?
- A. custom binary format
- B. XML
- C. CSV
- D. JSON
Answer: D
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks.html
NEW QUESTION # 38
Which three types of permissions can be granted to a user to callaborate and access a workspace in Oracle Machine Learning Notebooks?
- A. Manager
- B. Guest
- C. Developer
- D. Viewer
- E. Administrator
Answer: A,C,D
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.
NEW QUESTION # 39
Which type of user has access to the Oracle Machine Learning User Management interface?
- A. Developer
- B. Manager
- C. Guest
- D. Administrator
Answer: D
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/access-autonomous-database.htm
NEW QUESTION # 40
In which three use cases are Oracle Machine Learning algorithms suitable? (Choose three.)
- A. Speech recognition
- B. Graph analytics
- C. Customer segmentation
- D. Medical outcome analysis
- E. Anomaly and fraud detection
Answer: C,D,E
Explanation:
* 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:
* Medical outcome analysis: This is a use case that involves predicting the outcome of a medical treatment or procedure based on patient characteristics and medical history. Oracle Machine Learning algorithms such as Generalized Linear Models, Support Vector Machines, or Neural Networks can be used for this task.
* Anomaly and fraud detection: This is a use case that involves identifying unusual or suspicious patterns or behaviors in data that may indicate fraud, abuse, or errors. Oracle Machine Learning algorithms such as One-Class Support Vector Machines, Anomaly Detection, or Principal Component Analysis can be used for this task.
* Customer segmentation: This is a use case that involves grouping customers based on their similarities in terms of demographics, preferences, behaviors, or needs. Oracle Machine Learning algorithms such as K-Means, Expectation Maximization, or Non-Negative Matrix Factorization can be used for this task.
NEW QUESTION # 41
You want to predict which customers are likely to increase spending if given an additional credit card. Your task is to build a model using demographic and aggregated credit card data for customers who have used similar cards in the past. Which machine learning technique should you use to achieve this?
- A. Attribute Importance
- B. Feature Extraction
- C. Regression
- D. Classification
Answer: A
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/using-oracle-machine-learnin 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.
https://docs.oracle.com/en/database/oracle/machine-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.
NEW QUESTION # 42
When managing models using the Model Repository screen from the OML AutoML UI, what are the four operations a user can do to models and model deployments? (Choose four.)
- A. Deploy an existing Oracle Machine Learning in-database model as a REST endpoint in OML Services.
- B. Change the namespace of a previously deployed OML in-database model.
- C. Delete an existing OML in-database model.
- D. Undeploy a previously deployed OML in-database model.
- E. Change the owner of a previously deployed OML in-database model.
- F. Change the deployment date of a previously deployed OML in-database model.
Answer: A,B,C,D
Explanation:
Explanation
https://learn.oracle.com/ols/course/using-oracle-machine-learning-with-autonomous-database/35644/98086/1493
NEW QUESTION # 43
Which statement is FALSE about Oracle Machine Learning (OML) Notebooks?
- A. You can share notebooks with Import/Export operations.
- B. When visualizing a 1 million row database data using the built-in Zeppelin visualizers, OML will by default display the results on the entire table.
- C. Within notebook paragraphs you can switch between data views of tables, pie charts, bar charts, line plots and scatter plots.
- D. You can set the output format in SQL paragraphs of a notebook.
Answer: B
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/get-started-oracle-machine-le Typical Workflow For Using Notebooks To begin with Oracle Machine Learning Notebooks, refer to the tasks listed in the table as a guide. TasksMore InformationAccess Oracle Machine Learning Note-booksAccess Oracle Machine LearningCreate workspacesCreate Projects and WorkspacesCreate projectsCreate Projects and WorkspacesCreate notebooksCreate a NotebookRun a Notebook with Python InterpreterRun a Notebook with Python InterpreterUse the ScratchpadUse the Scratchpad-Create jobs to schedule notebooksCreate Jobs to Schedule Notebook
NEW QUESTION # 44
Which two statements are true about Classification algorithms? (Choose two.)
- A. They extract rules using unsupervised learning.
- B. They require known outcomes to guide the learning process.
- C. They assign cases to target categories.
- D. They predict numeric values along a continuum.
Answer: B,C
Explanation:
* 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
NEW QUESTION # 45
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