1z0-1041-23 Questions and Answers

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

Leaf is the relational database schema that consists of single-dimension tables in Oracle Analytics Cloud. A leaf schema is a type of schema that consists of one fact table and one dimension table for each dimension in the data model. A leaf schema simplifies the data model by eliminating the need for multiple tables or joins for each dimension. However, a leaf schema may result in data redundancy and inconsistency, as the same dimension values may be repeated across different tables. The other types of schemas, such as network, star, and snowflake, consist of more than one dimension table per dimension. A network schema consists of multiple fact tables and multiple dimension tables that are interconnected by foreign keys. A star schema consists of one fact table and multiple dimension tables that are directly related to the fact table by foreign keys. A snowflake schema consists of one fact table and multiple dimension tables that are related to each other by foreign keys. References: [Oracle Help Center], [Oracle Help Center]

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.

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

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]

After creating a data flow, you need to save the output data to a destination. You have two options to save data in Data Flow:

• Data Set Storage: This option allows you to save the output data as a data set in Oracle Analytics Cloud. You can choose the name, description, and format of the data set. You can also specify whether to overwrite or append the existing data set.
• Database Connection: This option allows you to save the output data to an external database that you have connected to Oracle Analytics Cloud. You can choose the connection, schema, table name, and mode of the database destination. References: [Oracle Help Center], [Oracle Help Center]

The correct use of BI Ask is that it is used to enable user input in natural language. It extracts column names from the user input and quickly builds visuals for the columns extracted. BI Ask is a feature that allows you to create analyses using natural language queries instead of dragging and dropping columns from the subject area pane. You can type or speak your query in BI Ask and it will automatically generate the best visualization for your query based on the columns extracted from your input. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

Government PDF forms is an example of the bursting feature in Pixel Perfect Reporting in Oracle Analytics Cloud. Pixel Perfect Reporting is a feature that allows you to create and publish highly formatted reports that meet specific layout and design requirements, such as invoices, statements, receipts, labels, and more. You can use Pixel Perfect Reporting to create reports using various tools and options, such as templates, layouts, components, expressions, parameters, and more. You can also use Pixel Perfect Reporting to publish reports using various methods and formats, such as email, printer, file system, FTP server, PDF, HTML, RTF, Excel, PowerPoint, and more. The bursting feature is a feature that allows you to distribute reports to multiple recipients based on certain criteria or conditions. You can use the bursting feature to create personalized reports for each recipient that contain only relevant information for them. You can also use the bursting feature to deliver reports to each recipient using their preferred method and format. Government PDF forms is an example of the bursting feature in Pixel Perfect Reporting in Oracle Analytics Cloud. You can use this feature to create and distribute government PDF forms that comply with specific standards and regulations for each recipient based on their location or status. The other options, such as ad hoc reporting, customized regional marketing reports, interactive data exploration, and bar codes are not examples of the bursting feature in Pixel Perfect Reporting in Oracle Analytics Cloud. These options are either not related to Pixel Perfect Reporting or describe other types of reports or features that can be created or used in Oracle Analytics Cloud. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

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]

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

A column’s navigation option can be enabled conditionally and presentation variables can be used while navigating from one analysis to another are two true statements about Action Links in Oracle Analytics Cloud. Action Links are a feature that allows you to create interactive links in your analyses that perform actions when clicked, such as navigating to another analysis, opening a web page, sending an email, or running a script. You can create Action Links for columns or measures in your analyses and configure them to suit your needs. You can enable a column’s navigation option conditionally by using a conditional expression that determines whether the Action Link is active or not based on the value of the column or another column in the same row. You can also use presentation variables while navigating from one analysis to another by passing the value of the presentation variable as a parameter to the target analysis. Presentation variables are variables that capture user input and store it in a session variable that can be referenced by other analyses or filters. The other statements, such as navigation from one analysis to another is not possible if they reside in different folders and navigation from one analysis to another works only when both analyses are created from a single subject area, are not true about Action Links in Oracle Analytics Cloud. You can navigate from one analysis to another regardless of where they reside in the catalog, as long as you have access to them. You can also navigate from one analysis to another even if they are created from different subject areas, as long as they have compatible columns or measures that can be used for filtering or drilling. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

To display the report in the click event drilled down in the same window, you can set the drill in place option in section properties. This option allows you to replace the original report with the drilled report in the same section of the dashboard page. You can also use breadcrumbs to navigate back to the original report. 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

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

ETS, Seasonal ARIMA, and Seasonal ETS are three models that are available for calculating the forecast in Oracle Analytics Cloud. A forecast is a feature that allows you to predict future values of your data based on historical data and various statistical methods. You can add a forecast to your visualization by selecting Forecast from the visualization gallery and choosing the data elements that you want to forecast. You can also adjust the forecast settings, such as the forecast length, confidence interval, and forecast method in the properties panel. You can choose from different models for calculating the forecast, such as ETS, Seasonal ARIMA, and Seasonal ETS. ETS stands for Exponential Smoothing, which is a model that uses weighted averages of past observations to smooth out the fluctuations and trends in the data. Seasonal ARIMA stands for Seasonal AutoRegressive Integrated Moving Average, which is a model that uses a combination of autoregressive and moving average terms to capture the patterns and seasonality in the data. Seasonal ETS stands for Seasonal Exponential Smoothing, which is a model that extends the ETS model by adding a seasonal component to account for the periodic variations in the data. The other models, such as ARENA and Seasonal ARENA, are not available for calculating the forecast in Oracle Analytics Cloud. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

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