[2023] Pass Key features of 1z0-1041-23 Course with Updated 72 Questions [Q17-Q35]

1z0-1041-23 Oracle

admin • November 8, 2023

[2023] Pass Key features of 1z0-1041-23 Course with Updated 72 Questions [Q17-Q35]

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[2023] Pass Key features of 1z0-1041-23 Course with Updated 72 Questions

1z0-1041-23 Sample Practice Exam Questions 2023 Updated Verified

Oracle 1z0-1041-23 Exam Syllabus Topics:

Topic	Details
Topic 1	Explain Migration Options to OAC and OAS Describe ‘sequence’ in the context of OAC data preparation
Topic 2	Register and apply Oracle Database Machine Learning Models Explain Advanced Calculations inside Expression Editor
Topic 3	Oracle Analytics Cloud Provisioning and Lifecycle Describe and Create Oracle Analytic Cloud Solutions
Topic 4	Explain OAC ‘best visualization’ for a data set Oracle Analytics Cloud Answers, Dashboards, and BI Publisher
Topic 5	Using Oracle Applications Connector and EPM Connector Create Prompts, Dashboards, and Calculations

QUESTION 17
You have a Historical Sales data set, which you use to train a model in Oracle Anatytics Cloud (OAC) Your products have an attribute named Profitability, which has one of the three values: high, medium, c This is the property you set as the target.
Which train data step is required to train a model in

Train Clustering

Train Text Prediction

Train Multi-Classifier

Train Multi-Value

Train Numeric Prediction

Train Binary-Classifier

A multi-classifier is a machine learning model that can predict one of several possible classes for a given input. In this case, the target attribute Profitability has three possible values: high, medium, or low. Therefore, a multi-classifier is the appropriate model to train on this data set. Reference: Oracle Analytics Cloud – Data Visualization User’s Guide, Oracle Analytics Cloud – Data Visualization User’s Guide

QUESTION 18
What does a single transaction mean in a sequence of data flows?

Multiple data flows in a sequence run one after the other. However, if any now falls, all the changes made in the sequence are rolled back.

A sequence can have only one data flow to save data to multiple data storage.

If any flow within a sequence fails, all the changes made in the sequence remain as is.

A data flow can load to a single data set at one point.

A single transaction means that all the data flows in a sequence are executed as a unit of work. If any data flow fails, the entire sequence is aborted and the changes made by the previous data flows are rolled back to ensure data consistency and integrity. Reference: Oracle Analytics Cloud – Data Visualization User’s Guide

QUESTION 19
Which two filters can be applied to all Dimensional data types?

List

Date

Expression

Range

List and Expression are two types of filters that can be applied to all Dimensional data types in Oracle Analytics Cloud. A List filter allows you to select one or more values from a list of values for a dimension column. An Expression filter allows you to create a custom filter expression using SQL syntax for a dimension column. The other types of filters, such as Date and Range, are only applicable to specific data types, such as Date and Numeric. Reference: [Oracle Help Center], [Oracle Help Center]

QUESTION 20
Which is a supported source for registering and using Machine Learning models7

Autonomous Data Warehouse

Mongo Database

SQL Server Oracle Data Science

Oracle Cloud Object Storage

Autonomous Data Warehouse is a supported source for registering and using machine learning models in Oracle Analytics Cloud. You can use the Register Model step in a data flow to register a machine learning model from Autonomous Data Warehouse and use it in your analyses or visualizations. You can also use the Apply Model step to apply a registered model to a new data set and generate predictions. Reference: Oracle Analytics Cloud – Data Visualization User’s Guide, Oracle Analytics Cloud – Data Visualization User’s Guide

QUESTION 21
You are a developer designing a solution to analyze retail sales. A key requirement for the solution is a product hierarchy with multiple levels that supports drill-down in an Oracle Analytics Cloud (OAC) workbook.
Which two configurations are appropriate for your solution?

Data set created with data preparation

Semantic model created in the OA Admin tool

Semantic model created in the Thin Client Modeler

Hierarchy created in a proper snowflake multi-table data set

Data set with a hierarchy created in OAC Data Flow

Data set merging a semantic model with the hierarchy and additional data

Semantic model created in the OA Admin tool and hierarchy created in a proper snowflake multi-table data set are two configurations that are appropriate for designing a solution to analyze retail sales with a product hierarchy in Oracle Analytics Cloud. A semantic model is a logical representation of your data that defines the relationships, calculations, hierarchies, and security rules for your data. You can create and manage semantic models using the OA Admin tool, which is a web-based interface that allows you to administer Oracle Analytics Cloud instances and services. A hierarchy is a structure that organizes your data into different levels of detail based on parent-child relationships. You can create hierarchies for your dimensions to enable drill-down analysis on your data. A snowflake multi-table data set is a type of data set that consists of multiple tables that are related to each other by foreign keys. You can create a snowflake multi-table data set using Data Preparation or Data Flow features in Oracle Analytics Cloud. You can use this type of data set to model complex dimensions that have multiple attributes stored in different tables, such as a product hierarchy with multiple levels. Reference: Oracle Help Center, [Oracle Help Center], [Oracle Help Center]

QUESTION 22
How should you determine the accuracy of a Machine Learning Model created in Oracle Analytics Cloud (OAC)?

Run the Machine earning model with various filter values applied.

Open the inspect dialog for given Machine i earning model and review the F1 score.

Open the inspect dialog for a given Machine Learning model and review the p score.

Use the Debug option in the Data Flow where the model was created. Create Data Flow that includes a histogram.

To determine the accuracy of a machine learning model created in Oracle Analytics Cloud, you can open the inspect dialog for the given machine learning model and review the F1 score. The F1 score is a metric that measures the balance between precision and recall of a machine learning model, especially for binary classification problems. The F1 score ranges from 0 to 1, where 1 means perfect accuracy and 0 means no accuracy. Reference: [Oracle Analytics Cloud – Data Visualization User’s Guide], [Oracle Analytics Cloud – Data Visualization User’s Guide]

QUESTION 23
You have a global data set for oil princes for a period of time for each country.
However, you need to perform analyses on Asian countries for a period of one month Which two combinations of filters could you use to achieve this?

Expression and Date Range

Range and Expression

List and Range

Date Range and Top Bottom N

List and Date Range

To filter a global data set for oil prices for a period of time for each country based on Asian countries and one month period, you can use two combinations of filters:
Expression and Date Range: An expression filter allows you to create a custom filter expression using SQL syntax. You can use an expression filter to specify the condition for Asian countries based on the country column of your data set. A date range filter allows you to filter your data based on a start date and an end date. You can use a date range filter to specify the one month period based on the date column of your data set.
List and Date Range: A list filter allows you to filter your data based on one or more values from a list of values. You can use a list filter to select the Asian countries from the list of values in the country column of your data set. A date range filter allows you to filter your data based on a start date and an end date. You can use a date range filter to specify the one month period based on the date column of your data set. Reference: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

QUESTION 24
Oracle Applications Connector supports Oracle Fusion Applications Cloud.
Which is valid about Oracle Applications Connector?

When abating a connection, enter the URL for Oracle Fusion Applications.

When creating a connection, enter the Business Intelligence URL for Oracle Fusion Applications

The Active User’s Credentials option in the Create Oracle Application Connection dialog box.

It cannot be used with Oracle Applications Connector.

It cannot connect to on-premises Oracle BI Enterprise Edition.

It can be used with the Thin Client Modeler.

Oracle Applications Connector supports Oracle Fusion Applications Cloud, which is a suite of cloud applications that provide enterprise resource planning, human capital management, customer relationship management, and other functionalities. To create a connection to Oracle Fusion Applications Cloud using Oracle Applications Connector, you need to enter the URL for Oracle Fusion Applications in the Create Oracle Application Connection dialog box. The other options are not valid about Oracle Applications Connector. You do not need to enter the Business Intelligence URL for Oracle Fusion Applications, as this is not required for the connection. The Active User’s Credentials option is not available in the Create Oracle Application Connection dialog box, as this is only applicable for some other types of connections. Oracle Applications Connector can be used with Oracle Applications Connector, as this is its purpose. It can also connect to on-premises Oracle BI Enterprise Edition, as this is one of the supported sources. It cannot be used with the Thin Client Modeler, as this is a separate tool that does not require a connection. Reference: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

QUESTION 25
Your client has created new custom map layer. How can this map layer be exposed in a project?

Create a new project. Add a map visualization. Change the layer property to new. Click upload. Select the Json file to upload.

Create a new project. Add a map visualization, change the layer property to new. click upload. Select the geojson file to upload.

Navigate to the Console. Click Maps. From the Map Layers tab, upload the geojson file.

Navigate to the Console. Click Maps. On the Map Layers tab, upload the xml file.

To expose a new custom map layer in a project, you need to do the following steps:
Navigate to the Console by clicking the Home icon on the top left corner of the screen and then clicking Console.
Click Maps on the left navigation pane to open the Maps page.
On the Map Layers tab, click Upload to upload your custom map layer file. The file must be in XML format and follow the Oracle Analytics Cloud map layer specification.
After uploading your custom map layer file, you can see it in the list of available map layers and use it in your projects. Reference: Oracle Analytics Cloud – Data Visualization User’s Guide, [Oracle Analytics Cloud – Data Visualization User’s Guide]

QUESTION 26
Which three statements are true about usage tracking in Oracle Analytics Cloud?

It is available only for Enterprise Edition.

You create a new connection pool in Data Modeler.

You create a new connection pool in the Oracle Analytics Developer Client tool.

After a change has been made to the Usage Tracking Connection Pool setting in the console, a restart is required.

It is available for both Professional and Enterprise Edition.

Usage tracking is a feature of Oracle Analytics Cloud that enables you to collect and analyze information about how users interact with your analytics content and resources. Usage tracking is available only for Enterprise Edition, as this is a more advanced and scalable edition than Professional Edition. To enable usage tracking, you need to create a new connection pool in the Oracle Analytics Developer Client tool, which is a desktop application that allows you to create and manage data models for Oracle Analytics Cloud. You also need to configure the Usage Tracking Connection Pool setting in the console, which is a web-based interface that allows you to administer Oracle Analytics Cloud instances and services. After making any change to this setting, you need to restart your Oracle Analytics Cloud instance for the change to take effect. Reference: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

QUESTION 27
As part of an analysis, you have the following set of data:

Assuming, you have entered the Aggregate function SUM (Revenue) in the second column.

Row 1: 2000 Row 2: 3000

Row 1: 1000 Row 2: 3000

Row 1: 1000 Row 2: 2000

Row 1; 3000 Row 2: 3000

Based on the image of the chart, the aggregate function SUM (Revenue) will calculate the sum of revenue for each product category. The chart shows that Product A has two rows with revenue values of 500 and 500, so the sum is 1000. Product B has two rows with revenue values of 1000 and 1000, so the sum is 2000. Reference: Oracle Analytics Cloud – Data Visualization User’s Guide

QUESTION 28
You have been tasked with building an analysis that requires data from two subject areas. How do you accomplish this?

Create two separate analyses and use the merge feature.

A subject area must be modified to include all the columns required.

Create an analysis and add a second subject area for a union.

Create two separate analyses and use the union feature.

To build an analysis that requires data from two subject areas, you can create an analysis and add a second subject area for a union. A union is an operation that combines two or more data sets with similar columns into one data set. You can use the Add Subject Area option in the Criteria tab of an analysis to add another subject area and select Union as the operation type. Reference: [Oracle Analytics Cloud – Data Visualization User’s Guide], [Oracle Analytics Cloud – Data Visualization User’s Guide]

QUESTION 29
The Administrator has navigated to the console page and clicked on search index. Which three items can be included in the index?

User Folders

Templates

Shared Folders

Data Models

Content Folder

User Folders, Shared Folders, and Data Models are three items that can be included in the search index in Oracle Analytics Cloud. The search index is a feature that enables you to quickly find and access the analytics content and resources that you need. You can configure the search index to include or exclude certain types of items from the index, such as folders, data sets, projects, data models, and more. You can include User Folders and Shared Folders in the search index to make them searchable by name, description, or owner. You can also include Data Models in the search index to make them searchable by name, description, owner, or subject area. The other item, Templates, cannot be included in the search index, as this is not a supported type of item for indexing. Reference: [Oracle Help Center], [Oracle Help Center]

QUESTION 30
You are creating a data model for a healthcare provider Patient information is contained in related tables for contact information, primary physician, insurance information, and billing details.
For management and personal information reasons, the project owner requires that these table not be merged.
What is the correct relational topology to model the patient dimension?

Distributed tree

Universe

Balance tree

Start

Balance tree is the correct relational topology to model the patient dimension for a healthcare provider in Oracle Analytics Cloud. A balance tree is a type of schema that consists of one or more dimension tables that are related to each other by foreign keys. A balance tree schema allows you to model a dimension that has multiple attributes that are organized into different levels of detail. For example, you can model a patient dimension that has attributes such as contact information, primary physician, insurance information, and billing details. Each attribute can be stored in a separate table that is linked to a parent table by a foreign key. A balance tree schema enables you to perform drill-down analysis on your dimension without having to merge all the tables into one. The other types of schemas, such as distributed tree, universe, and star, are not suitable for modeling a patient dimension with multiple related tables. A distributed tree schema consists of one or more dimension tables that are not related to each other by foreign keys. A universe schema consists of one or more fact tables that are related to each other by foreign keys. A star schema consists of one fact table that is related to multiple dimension tables by foreign keys. Reference: [Oracle Help Center], [Oracle Help Center]

QUESTION 31
Which two can be defined on a canvas in a story in Oracle Analytics Cloud?

Narration

Notes

Notifications

Filters

Notifications and Filters are two elements that can be defined on a canvas in a story in Oracle Analytics Cloud. A canvas is a page or a slide where you can add and arrange various elements to create your story. You can add multiple canvases to your story and navigate between them using the navigation bar. You can define Notifications and Filters on a canvas to enhance your story and make it more interactive. Notifications are messages that appear on your canvas to inform or alert your audience about something important or relevant to your story, such as a key insight, a recommendation, or a call to action. You can create notifications using the Notification icon on the toolbar and customize them by changing the text, color, position, and duration of the message. Filters are criteria that limit the data that is displayed on your canvas based on certain values or ranges. You can create filters using the Filter icon on the toolbar and apply them to one or more elements on your canvas. You can also enable your audience to change the filter values interactively using the filter bar or the filter dialog box. The other elements, such as Notes and Narration, are not available on a canvas in a story in Oracle Analytics Cloud. Reference: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

QUESTION 32
In which two layers can you use Oracle Analytics Cloud (OAC) to determine which values of a given data set may provide the greatest opportunity due to their outlier qualities?

Add an outlier visualization to your canvas. This will highlight data points that stand out and may justify further investigation.

Modify the color palette to highlight unique values.

Use a combination of Max/Min value reference lines.

Create a box plot visualization of your data set.

Oracle Analytics Cloud (OAC) provides various ways to identify and analyze outliers in your data set. Outliers are data points that deviate significantly from the rest of the data and may indicate errors, anomalies, or opportunities. Two layers that you can use to determine which values of a given data set may provide the greatest opportunity due to their outlier qualities are:
Add an outlier visualization to your canvas. This will highlight data points that stand out and may justify further investigation. An outlier visualization is a type of scatter plot that automatically detects and labels outliers in your data using machine learning algorithms. You can use the outlier visualization to explore the characteristics and causes of the outliers and compare them with the normal data points.
Create a box plot visualization of your data set. A box plot is a type of graph that shows the distribution of a numerical variable using quartiles and whiskers. A box plot can also identify outliers as data points that lie beyond the whiskers or outside a certain range. You can use the box plot to see how the outliers affect the overall distribution and variation of your data.

QUESTION 33
Which two statements are true about Oracle Analytics Cloud (OAC) self-service data preparation

Database data can be extended with spreadsheet data.

The Oracle Data Integrator (ODI) tool, which is used for extract, load, and transformation, foundational component of Oracle Analytics Cloud and is essential for data preparation.

Data flows have many built-in steps that can be used to prepare the data.

Joins can be created only in Oracle Analytics Cloud Data Modeler.

Oracle Analytics Cloud (OAC) self-service data preparation is a feature that allows you to transform and enrich data from various sources and create data sets for analysis or visualization. Some statements that are true about OAC self-service data preparation are:
Database data can be extended with spreadsheet data. This means that you can join or union data from a database source with data from a spreadsheet file and create a new data set that combines both sources.
Data flows have many built-in steps that can be used to prepare the data. This means that you can use data flows to perform various operations on your data, such as creating calculated columns, filtering or sorting rows, grouping or aggregating values, applying machine learning models, or saving data to different formats. Reference: Oracle Analytics Cloud – Data Visualization User’s Guide, Oracle Analytics Cloud – Data Visualization User’s Guide

QUESTION 34
Using Narrate insight, you can take a snapshot of any moments of any information that you see in a visualization and keep track of any moments of sudden realization while you work with the data.
You can then share these with other users. Which statement is true about sharing with Insights?

It is not possible to share Insights with other users.

By default all users cannot share Insights.

Insights is a/ways shared in form of story so everybody will see the same information.

Insights is shared as a story and everybody will see their information based on permissions

Insights is always shared in form of story so everybody will see the same information and insights is shared as a story and everybody will see their information based on permissions are two true statements about sharing with insights in Oracle Analytics Cloud. Insights are messages that appear on your canvas to inform or alert your audience about something important or relevant to your story, such as a key insight, a recommendation, or a call to action. You can create insights using the Insight icon on the toolbar and customize them by changing the text, color, position, and duration of the message. You can also enable or disable the Use Snapshot Data option for each insight in the properties panel. When you share your insights with other users, you share them as part of your story, which is a feature that allows you to create and present a narrative based on your data using various elements, such as visualizations, text boxes, images, videos, and more. You can share your story with other users by exporting it as a file (.png, .pdf, or .dva) or by sending it via email or chat. When you share your insights as part of your story, everybody will see the same information that you see in your insights, regardless of whether you use live data or snapshot data for your insights. However, everybody will also see their information based on their permissions for accessing the data sets or projects that are associated with your story. The other statements, such as it is not possible to share insights with other users and by default all users cannot share insights, are not true about sharing with insights in Oracle Analytics Cloud. You can share insights with other users as part of your story using various methods, such as exporting, emailing, or chatting. You can also control who can share insights by changing the permissions of your story in the catalog. Reference: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

QUESTION 35
How can Oracle Analytics Cloud (OAC) be used to categorize a large number of data points on a particular canvas?

Create a trend line and apply OAC Advanced Analytics.

Create a cluster with a suitable number of groups for the specific analysis.

Visualize the data by using a network chart.

Use a combination of a tree diagram and a trellis visualization.

Creating a cluster with a suitable number of groups for the specific analysis is a method that you can use to categorize a large number of data points on a particular canvas in Oracle Analytics Cloud. A cluster is a group of data points that have similar characteristics or patterns based on certain criteria or variables. Clustering is a machine learning technique that allows you to automatically segment your data into clusters based on various algorithms and techniques, such as k-means, hierarchical, or density-based clustering. You can create a cluster with a suitable number of groups for your specific analysis by selecting Cluster from the visualization gallery and choosing the data elements that you want to use for clustering. You can also adjust the number of groups and the clustering method in the properties panel. You can use this method to categorize a large number of data points on your canvas and discover hidden patterns or relationships in your data. The other methods, such as creating a trend line, visualizing the data by using a network chart, or using a combination of a tree diagram and a trellis visualization, are not suitable for categorizing a large number of data points on a particular canvas in Oracle Analytics Cloud. These methods are either not supported or not optimal for clustering or categorizing data in Oracle Analytics Cloud. Reference: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

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