[Dec 26, 2023] 1z0-1096-23 Exam Dumps, 1z0-1096-23 Practice Test Questions
Free 1z0-1096-23 Study Guides Exam Questions and Answer
NEW QUESTION # 15
In which three use cases are Oracle Machine Learning algorithms suitable? (Choose three.)
- A. Medical outcome analysis
- B. Anomaly and fraud detection
- C. Customer segmentation
- D. Graph analytics
- E. Speech recognition
Answer: A,B,C
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 # 16
Which two statements are true about supervised machine learning? (Choose two.)
- A. There is no previously known result to guide the algorithm in building the model.
- B. It does not specify a target, it can be applied to a population of interest.
- C. It is used to extract meaningful insights from raw data to improve data operational efficiency.
- D. The learning process is directed by a previously known dependent attribute or target.
- E. It generally results in predictive models.
Answer: D,E
Explanation:
Explanation
https://docs.oracle.com/cd/F32587_01/dmcon/oracle-machine-learning-sql-concepts.pdf
NEW QUESTION # 17
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 # 18
Which two can be performed by an Administrator in Oracle Machine Learning Notebooks? (Choose two.)
- A. Run noteboooks
- B. Reassign user workspace
- C. View notebooks in read-only mode
- D. Manage any notebook
Answer: B,C
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/administer-oracle-machine-le
NEW QUESTION # 19
Which three types of templates are available in Oracle Machine Learning Notebooks? (Choose three.)
- A. Custom templates
- B. Personal templates
- C. Public templates
- D. Example templates
- E. Shared templates
Answer: B,D,E
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/use-library-collaborate-users
NEW QUESTION # 20
Which option would you use to load data from Object Storage into an Oracle Autonomous Database?
- A. Expdp
- B. DBMS_Cloud package
- C. SQL*Loader
- D. Oracle SQL Developer Web
Answer: B
Explanation:
Explanation
About Data Loading: Autonomous Database provides the following loading options: You can load data using Oracle Database Actions. You can load data using Oracle Database tools and Oracle or other 3rd party data integration tools. On transaction processing systems you traditionally ingest data through routine transactions or with DML operations. In general you load data from files local to your client computer or from files stored in a cloud-based object store. To load data from files in the cloud, use either Oracle Database Actions or use the Autonomous Database PL/SQL package DBMS_CLOUD to load files from the cloud. For the fastest data loading experience Oracle recommends uploading the source files to a cloud-based object store, such as Oracle Cloud Infrastructure Object Storage, before loading the data into your database. Oracle provides support for loading files that are located locally in your data center, but when using this method of data loading you should factor in the transmission speeds across the Internet which may be significantly slower. For more information on Oracle Cloud Infrastructure Object Storage, see Putting Data into Object Storage and Overview of Object Storage. Note: If you are not using ADMIN user, ensure the user has the necessary privileges for the operations the user needs to perform. See Manage User Privileges on Autonomous Database - Connecting with a Client Tool for more information.
NEW QUESTION # 21
What is the correct sequence of function invocations of AutoML API from OML4Py to solve a business problem?
- A. Model selection, Algorithm selection
- B. Model selection, Algorithm selection
- C. Algorithm selection, Feature selection. Model tuning
- D. Model tuning. Algorithm selection. Feature selection
Answer: C
Explanation:
Explanation
https://www.doag.org/formes/pubfiles/13151859/OE-DAC-Oracle-Machine-Learning-Overview-Whats-New-Cu
NEW QUESTION # 22
Which two templates are supported while saving a notebook? (Choose two.)
- A. Personal
- B. Shared
- C. Example
- D. Public
Answer: A,B
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/notebooks-templates.html
NEW QUESTION # 23
Which three are unsupervised machine learning algorithms? (Choose three.)
- A. Naive Bayes
- B. K-means clustering
- C. Association rule
- D. Principal Component Analysis
- E. Random Forest
- F. Logistical Regression
Answer: B,C,D
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 # 24
What is the proper workflow for analyzing data in Oracle Machine Learning?
- A. Build the model, prepare the data, evaluate the model, and deploy the model.
- B. Get predictions from the model, prepare the data, build the model, and deploy the model.
- C. Prepare the data, build the model, evaluate the model, and deploy the model.
- D. Evaluate the model, prepare the data, build the model, and deploy the model.
Answer: C
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/process-overview.html#GUID-A6 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
NEW QUESTION # 25
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. Clustering
- B. Classification
- C. Feature Extraction
- D. Regression
Answer: A
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 # 26
For which two types of notebooks can you schedule a job? (Choose two.)
- A. Notebooks owned by you
- B. Notebooks under Personal templates
- C. Notebooks under Shared templates
- D. Notebooks shared with you
Answer: A,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: Cre-ate, 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 # 27
Which four actions would typically be performed during the data preparation step for analyzing data with Oracle Machine Learning?
- A. binning of numeric data
- B. performing feature engineering, such as creating derived variables
- C. data collection from various sources
- D. numeric data normalization
- E. building a machine learning model
- F. missing value replacement
Answer: A,B,D,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 # 28
How can you share a notebook with multiple developers for a collaborative effort with note-book editing?
- A. You can share notebooks if you have Viewer permissions.
- B. Notebooks cannot be shared for collaborating with other users.
- C. You can share notebooks if you have Developer permissions.
- D. You create different notebooks, edit separately, and merge later.
Answer: C
Explanation:
Explanation
You can also collaborate by exporting your notebook as a JSON or JavaScript Object Notation file. This exported file can be imported into the same or different environment. To export a notebook as a JSON file, open the notebook in the notebook editor, click on the Export icon. A Save As dialog will open where you can specify the name of the JSON file and location on your system.
NEW QUESTION # 29
When running which type of code must you use %script in Oracle Machine Learning Note-books?
- A. PL/SQL script
- B. JavaScript
- C. JSON script
- D. SQL script
Answer: A
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/use-scratchpad.html
NEW QUESTION # 30
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. Attribute Importance
- C. Feature Extraction
- D. Regression
Answer: B
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 # 31
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. Notebook
- B. Project
- C. Workspace
- D. Job
Answer: A
Explanation:
Explanation
https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/create-notebooks-oml.html#GUID-0F489564
NEW QUESTION # 32
Examine the SQL command: SQL> SELECT
CUST_MARITAL_STATUS,MEDIAN(CUST_CREDIT_LIMIT) FROM SH.CUSTOMERS GROUP BY CUST_MARITAL_STATUS; Which statement is true about running this command where the table has columns cust_maritax._status3 (VARCHAR2) and cust_credit_limit (number) with 100k rows of non-null data?
- A. It will return the marital status and credit limit of each customer.
- B. It will return the median value of customer credit limit grouped by marital status.
- C. It will return a customer's marital status and credit limit grouped by marital status
- D. It will return an error.
Answer: B
Explanation:
* 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.
NEW QUESTION # 33
You want to analyze the spike in sales during weekends in India (Saturday and Sunday) and also perform a similar analysis for weekends in Dubai (Friday and Saturday). You will be re-using these notebooks in similar situations over time. You do not want to share them with other users. Which template should you use?
- A. Shared
- B. Personal
- C. Example
- D. Public
Answer: B
Explanation:
Explanation
https://learn.oracle.com/ols/course/using-oracle-machine-learning-with-autonomous-database/35644/98086/1493
NEW QUESTION # 34
Which is a FALSE statement regarding Oracle Machine Learning (OML)?
- A. OML offerings need a separate data visualization tool for creating visualization.
- B. OML provides univariate and multivariate statistics.
- C. OML provides integration with open source Python and R statistical analysis functions.
- D. OML provides scalable statistical functions though OML4Py and OML4R.
Answer: A
Explanation:
* 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
NEW QUESTION # 35
Which output formats are supported by the SET SQLFORMAT command? (Choose three.)
- A. HTML
(Correct) - B. TXT
- C. JSON
- D. CSV
Answer: C,D
Explanation:
Explanation
https://docs.oracle.com/en/database/oracle/machine-learning/oml-notebooks/omlug/output-formats-supported-set
NEW QUESTION # 36
What is the correct sequence of creating items in Oracle Machine Learning (OML) Note-books when setting up a new Autonomous Database instance?
- A. Workspace, OML User, Notebook, Jobs
- B. OML User, Notebook, Job
- C. Notebook, Job, Project, OML User
- D. Job, Project, Workspace, Notebook
Answer: A
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 # 37
Which two Oracle Cloud services are Integrated with Oracle Machine Learning? (Choose two.)
- A. Oracle Stream Analytics
- B. Oracle Analytics Cloud
- C. Oracle Autonomous Database
- D. Oracle GoldenGate
Answer: B,C
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 # 38
Which three types of forms are available in Oracle Machine Learning Notebooks? (Choose three.)
- A. Text Input form
- B. Radio form
- C. Check Box form
- D. Select form
- E. List form
Answer: A,C,D
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 # 39
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