2024 Latest 100% Exam Passing Ratio - 1z0-1096-23 Dumps PDF
Pass Exam With Full Sureness - 1z0-1096-23 Dumps with 73 Questions
Oracle 1z0-1096-23 Exam Syllabus Topics:
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NEW QUESTION # 21
When managing Machine Learning models using the OML AutoML UI, what are the three actions that can be performed on Oracle Machine Learning (OML) models from within OML AutoML UI? (Choose three.)
- A. Review the model statistics associated with the experiment of an ONNX-format image classification model.
- B. Deploy an OML in-database model to a REST endpoint.
- C. Review the model statistics associated with the experiment of an ONNX-format (Open Neural Networks Exchange) model.
- D. Create a notebook with auto-generated OML4Py code from an OML in-database mod-el to allow for further model tweaking and batch scoring.
- E. Review the model statistics associated with the experiment of an OML in-database model.
Answer: B,D,E
Explanation:
Explanation
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.
NEW QUESTION # 22
Examine the command: %script SET SQLFORMAT ANSICONSOLE What is the output of a query if this SQLFORMAT is set?
- A. It formats the output columns to return ANSI characters.
- B. It formats output columns with fixed-width columns with data enclosed in double quotation marks.
- C. It formats and resizes the output columns to the width of the data to save space.
- D. It formats the output columns to Add ANSI characters as the delimiter string.
Answer: C
Explanation:
Explanation
https://docs.oracle.com/en/cloud/paas/autonomous-data-warehouse-cloud/omlug/output-formats-supported-set-sq
NEW QUESTION # 23
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. Classification
- B. Feature Extraction
- C. Attribute Importance
- D. Regression
Answer: C
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 # 24
Which three types of permissions can be granted to a user to callaborate and access a workspace in Oracle Machine Learning Notebooks?
- A. Viewer
- B. Developer
- C. Administrator
- D. Guest
- E. Manager
Answer: A,B,E
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 # 25
Which three data sources can be immediately used for model building in Oracle Machine Learning? (Choose three.)
- A. JSON files
- B. Oracle Database views
- C. Oracle Database External tables
- D. CSV files
- E. Oracle Database tables
Answer: A,D,E
Explanation:
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
NEW QUESTION # 26
What are three key features of Oracle Machine Learning Notebooks? (Choose three.)
- A. They support integration with Oracle Data Miner-ID
- B. They provide a collaborative notebook interface on Oracle Autonomous Database.
- C. They enable job scheduling of notebooks on a recurring schedule.
- D. They support SQL, PL/SQL, JavaScript, and PHP scripting languages.
- E. They enable access to in database implementation of machine learning algorithms.
Answer: B,C,E
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/
NEW QUESTION # 27
To navigate to a specific notebook provided by another user, which is the correct workflow?
- A. Select workspace > Select project > List notebooks > Open notebook
- B. Change user > Open notebook > Create project
- C. Create user > Change workspace > Open notebook
- D. Change project > Change workspace > Open notebook
Answer: A
Explanation:
Explanation
https://apexapps.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?wid=891&clear=180&session=109742845
NEW QUESTION # 28
Which two Oracle Cloud services are Integrated with Oracle Machine Learning? (Choose two.)
- A. Oracle GoldenGate
- B. Oracle Analytics Cloud
- C. Oracle Stream Analytics
- D. Oracle Autonomous Database
Answer: B,D
Explanation:
* 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.
NEW QUESTION # 29
Which output formats are supported by the SET SQLFORMAT command? (Choose three.)
- A. HTML
(Correct) - B. CSV
- C. TXT
- D. JSON
Answer: B,D
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/output-formats-supported-set
NEW QUESTION # 30
When running which type of code must you use %script in Oracle Machine Learning Note-books?
- A. SQL script
- B. PL/SQL script
- C. JavaScript
- D. JSON script
Answer: B
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/use-scratchpad.html
NEW QUESTION # 31
Which four statements are true about Oracle Machine Learning on Oracle Autonomous Database? (Choose four.)
- A. It provides an interface to monitor a database.
- B. It is deployed with Oracle Data Miner.
- C. It includes parallelized in-database algorithms.
- D. It enables data analytics, data discovery, and data visualizations.
- E. It provides a development environment to build models and score data.
- F. It provides a collaborative web-based notebook interface.
Answer: C,D,E,F
Explanation:
Explanation
https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/autonomous-oml.html#GUID-63F2D68B-6D
NEW QUESTION # 32
Which type of user has access to the Oracle Machine Learning User Management interface?
- A. Administrator
- B. Developer
- C. Manager
- D. Guest
Answer: A
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/access-autonomous-database.htm
NEW QUESTION # 33
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. Job
- B. Workspace
- 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 # 34
Which statement is FALSE about OML Notebooks?
- A. You must have the Administrator role to access the OML User Management Interface.
- B. The administrator is not authorized to run notebooks.
- C. An existing database user cannot be added as an OML user using the User Management Interface.
- D. You must have the Administrator role to access the compute resources page.
Answer: C
Explanation:
* 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.
NEW QUESTION # 35
You want to segment your customer data for marketing reseach purposes and identify homogeneous groups to build supervised models. What should you use to achieve this?
- A. Classification
- B. Feature Extraction
- C. Clustering
- D. Regression
Answer: C
Explanation:
* 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.
NEW QUESTION # 36
Which machine learning algorithm allows the system to identify patterns within data sets on its own?
- A. Unsupervised Learning
- B. Semi-supervised Learning
- C. Supervised Learning
- D. Reinforcement Learning
Answer: A
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/oracle-machine-learning-sql-guid
NEW QUESTION # 37
Which four actions would typically be performed during the data preparation step for analyzing data with Oracle Machine Learning?
- A. data collection from various sources
- B. building a machine learning model
- C. missing value replacement
- D. numeric data normalization
- E. performing feature engineering, such as creating derived variables
- F. binning of numeric data
Answer: C,D,E,F
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 # 38
Which three tasks can be performed by an Oracle Machine Learning (OML) user with the Developer role?
(Choose three.)
- A. Create and run OML notebooks.
- B. Edit Example templates.
- C. Create and run scratchpads.
- D. Share notebooks.
- E. Transfer OML workspace to other users.
- F. Create other OML users.
Answer: A,D,E
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/administer-oracle-machine-le
NEW QUESTION # 39
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