Oracle 1z0-1041-23 Dumps

Google Map and Baidu Map are two examples of maps that use an API key to add as map background in Oracle Analytics Cloud. An API key is a unique identifier that allows you to access and use these map services in your projects. You need to obtain an API key from the map provider and configure it in Oracle Analytics Cloud before you can use these maps as background layers. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

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. References: Oracle Analytics Cloud - Data Visualization User’s Guide, [Oracle Analytics Cloud - Data Visualization User’s Guide]

To create a relative time filter, you need to perform two steps:

• Select Relative Time as the filter type: A relative time filter allows you to filter your data based on a relative time period, such as last week, next month, or current year. You can select Relative Time as the filter type from the drop-down menu of the filter bar.
• Select Relative Time as the filter type and set the Relative To property: The Relative To property allows you to specify the reference point for your relative time period. For example, if you want to filter your data for the last quarter relative to today, you can set the Relative To property to Today. You can set the Relative To property from the filter dialog box after selecting Relative Time as the filter type. References: [Oracle Help Center], [Oracle Help Center]

The quickest way to start the analysis of employee attrition data after uploading it to Oracle Analytics Cloud is to use the Explain option on the Attrition attribute. The Explain option is a feature that allows you to automatically generate insights and visualizations about an attribute or a measure in your data set. In this case, the Explain option will show you the most important factors that contribute to employee attrition, such as salary, job satisfaction, or performance rating. References: Oracle Analytics Cloud - Data Visualization User’s Guide, [Oracle Analytics Cloud - Data Visualization User’s Guide]

Data Preparation in OAC uses obfuscation to protect sensitive information such as SSN, credit card number, and customer number. Obfuscation is a technique that replaces the original values with random or masked values that preserve the format and length of the original values. This way, you can hide or anonymize sensitive information while maintaining its usability for analysis or visualization. References: Oracle Analytics Cloud - Data Visualization User’s Guide, Oracle Analytics Cloud - Data Visualization User’s Guide

Two statements that are true about hierarchy and levels are:

• Each level is based on one or multiple columns. A level is a logical grouping of one or more columns that represent a certain granularity or detail of data. For example, a level can be based on a single column, such as Year, or multiple columns, such as Year and Quarter.
• Hierarchies enable drilling in reports. A hierarchy is a logical structure that defines the relationships among levels and shows how data can be aggregated or disaggregated from one level to another. For example, a hierarchy can show how Year, Quarter, Month, and Day are related in a time dimension. Hierarchies allow users to drill down or drill up in reports to see different levels of detail or summary of data. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

A fact table uses a foreign key to reference dimension tables in a relational database schema. A fact table is a table that contains numerical measures or facts that are related to a business process or event, such as sales, orders, or transactions. A dimension table is a table that contains descriptive attributes or dimensions that provide context to the facts, such as product, customer, time, or location. A foreign key is a column or a set of columns in a table that refers to the primary key of another table. A primary key is a column or a set of columns in a table that uniquely identifies each row in the table. A foreign key establishes a relationship between the fact table and the dimension tables, and allows you to join them for analysis. The other options, such as dimension attribute, aggregate type, and master key, are not used by a fact table to reference dimension tables. A dimension attribute is a column in a dimension table that contains a specific level or detail of the dimension, such as product name, customer name, or month. An aggregate type is a property of a measure column that specifies how the measure should be aggregated or calculated, such as sum, average, count, or minimum. A master key is not a valid term in relational database schema. References: Oracle Help Center, Oracle Help Center

To create an Oracle Analytics Cloud (OAC) instance, you need to subscribe to the Oracle Analytics Cloud service on Oracle Cloud Infrastructure (OCI). Oracle Analytics Cloud is a comprehensive analytics platform that provides a range of analytics capabilities, such as data visualization, augmented analytics, machine learning, enterprise reporting, and more. You do not need to subscribe to other services such as Oracle Back Storage, Oracle Autonomous Database, or Oracle Big Data Cloud Storage to create an OAC instance, although you can use them as data sources for your analytics projects. References: [Oracle Help Center], [Oracle Help Center]

Dashboard prompts are filters that allow users to dynamically change the data displayed in a dashboard. Some statements that are true about dashboard prompts are:

• Prompt values can be limited based on values selected in other prompts. This is called cascading prompts, where the values of one prompt depend on the selection of another prompt.
• Repository variables and session variables can be used in prompt default values. This allows users to set dynamic default values for prompts based on variables defined in the repository or the session. References: Oracle Analytics Cloud - Data Visualization User’s Guide, Oracle Analytics Cloud - Data Visualization User’s Guide

Drilling down and viewing data from multiple data sets in a single query are two features that are supported on the home page of a BI Ask visualization in Oracle Analytics Cloud. BI Ask is a natural language interface that allows you to ask questions and get answers in the form of visualizations. You can drill down on the visualizations to explore the data at different levels of detail. You can also view data from multiple data sets in a single query by using keywords such as “and”, “or”, and “with”. The other features, such as opening and saving visualizations, are not supported on the home page of a BI Ask visualization, but rather require you to open the visualization in a project or data visualization. References: [Oracle Help Center], [Oracle Help Center]

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. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

Bin is the data preparation action that helps you to create a column that labels numerical values as Large, Medium, or Small in Oracle Analytics Cloud. Bin is a feature that allows you to group numerical values into discrete categories or bins based on a specified range or interval. You can use Bin to create a new column that assigns labels to each bin, such as Large, Medium, or Small. The other data preparation actions, such as Convert, Extrapolate, and Bundle, do not help you to accomplish this task. Convert is a feature that allows you to change the data type or format of a column. Extrapolate is a feature that allows you to fill in missing values in a column based on a linear or exponential trend. Bundle is a feature that allows you to combine multiple columns into one column. References: [Oracle Help Center], [Oracle Help Center]

To enable a different set of report links at the page level and set different report links for different analyses, you need to do the following steps:

• Enable all the Dashboard Report links from Dashboard Properties. This will allow you to use report links such as drill down, drill up, drill across, or navigate to other dashboards or web pages.
• Set the Report links for each Reach report. This will allow you to customize which report links are available for each analysis on each dashboard page. References: Oracle Analytics Cloud - Data Visualization User’s Guide, Oracle Analytics Cloud - Data Visualization User’s Guide

To create a line chart visualization of revenue data at the day level that displays the last 365 days of data and the next 90 days of forecast, you need to add a forecast and change the Period value to 90. A forecast is a feature that allows you to predict future values of a measure based on historical data using various methods, such as linear, exponential, or seasonal. The Period value specifies how many future values you want to forecast. For example, you can add a forecast to your line chart and set the Period value to 90 to see the projected revenue for the next 90 days. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

Anomaly Detection is the algorithm that would be the best fit for creating a model that identifies customers with multiple potential fraudulent transactions in Oracle Analytics Cloud. Anomaly Detection is a machine learning technique that allows you to detect outliers or anomalies in your data that deviate from the normal or expected behavior. You can use Anomaly Detection to create a model that scores each customer based on their transaction history and flags those who have unusually high or low values as potential fraudsters. The other algorithms, such as Support Vector Machine, Decision Tree, and Logistic Regression, are not the best fit for this purpose. Support Vector Machine is a machine learning technique that allows you to classify data into two or more categories based on a linear or nonlinear boundary. Decision Tree is a machine learning technique that allows you to create rules or conditions for splitting data into branches or nodes based on certain criteria. Logistic Regression is a machine learning technique that allows you to predict the probability of an event occurring based on one or more variables. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

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. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

Data wrangling is a process of transforming and enriching data to make it more suitable for analysis or visualization. Some statements that are true about data wrangling are:

• In data wrangling, transformations available are dependent on the data type. For example, you can apply different transformations to numeric, text, date, or location data types.
• It is used to curate data sets that can be used for complex visualizations. For example, you can use data wrangling to create calculated columns, merge or split columns, group or aggregate values, filter or sort rows, or join or union data sets. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

A story can have multiple canvases and you can only export the active visual are two true statements about creating 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 also define notifications, filters, notes, and narration on each canvas to enhance your story and make it more interactive. You can only export the active visual when you create a story in Oracle Analytics Cloud. The active visual is the visualization that is currently selected or highlighted on your canvas. You can export the active visual as an image file (.png) by clicking on the Export icon on the toolbar and selecting Export Active Visual from the menu. You cannot export other elements or multiple visuals at once when you create a story in Oracle Analytics Cloud. The other statements, such as insights always take a snapshot data for a story and a project can have only one story, are not true about creating a story 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. Insights do not always take a snapshot data for a story, but rather use live data from your data set or project by default. You can change this behavior by enabling or disabling the Use Snapshot Data option for each insight in the properties panel. A project is a workspace where you can create and edit your data sets, visualizations, data flows, and stories using various tools and options. A project can have more than one story, as you can create multiple stories for different purposes or audiences within the same project. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

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. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

Sharing the story on Day by Day with their decision makers is the most likely reason why an analyst has created a story on the Narrate tab of the project in Oracle Analytics Cloud. A story 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 use a story to communicate your findings and recommendations to your audience in an engaging and interactive way. The Narrate tab is a workspace where you can create and edit your story using various tools and options. You can add multiple canvases to your story and navigate between them using the navigation bar. You can also add notifications, filters, notes, and narration to your story to enhance it and make it more interactive. Day by Day is a mobile application that allows you to access and interact with your Oracle Analytics Cloud content using voice or text commands. You can use Day by Day to view and explore your stories on your mobile device and share them with other users via email or chat. You can also receive personalized insights and recommendations from Day by Day based on your location, calendar, and preferences. Sharing the story on Day by Day with their decision makers is a convenient and effective way for an analyst to deliver their analysis and influence their decisions. The other options, such as sharing the story by exporting to MS Excel, sharing the story with consumers on the Home Page, and sharing the story by exporting to pdf, are not the most likely reasons why an analyst has created a story on the Narrate tab of the project in Oracle Analytics Cloud. These options are either not supported or not optimal for sharing a story in Oracle Analytics Cloud. References: Oracle Help Center, Oracle Help Center, Oracle Help Center