1z0-1041-23 Practice Exam Questions with Answers Oracle Cloud Infrastructure 2023 Enterprise Analytics Professional Certification

Data Flow is a feature of Oracle Analytics Cloud that enables you to create and run data flows to transform and enrich your data. You can use Data Flow for various use-cases, such as:

• To curate data sets that can be used for visualizations. You can join, filter, aggregate, pivot, and perform other operations on your data sets to prepare them for analysis.
• To train and test the Machine Learning model. You can use Data Flow to apply machine learning algorithms to your data and create predictive models. You can also evaluate the performance of your models and compare them with different parameters. References: [Oracle Help Center], [Oracle Help Center]

Two types of filters that can be used to display as prompts in your Publisher report are:

• Number filters. A number filter allows you to specify a numeric value or a range of numeric values for a column using operators such as equals, not equals, greater than, less than, or between. For example, you can use a number filter to prompt users to enter a minimum or maximum sales amount for your report.
• Expression filters. An expression filter allows you to specify a logical expression for a column using operators such as AND, OR, NOT, or parentheses. For example, you can use an expression filter to prompt users to enter a combination of conditions for your report, such as region equals South AND sales amount greater than 1 million USD. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

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

To edit and create a new semantic model in Oracle Analytics Cloud Enterprise Edition, you need to have the BI Data Model Author role. This role allows you to access and use the Data Modeler tool, where you can create and modify semantic models that define the logical structure and relationships of your data sources. You can also use the Data Modeler to create calculations, hierarchies, variables, security filters, and other features for your semantic models. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

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]

OBIEE 11.1.X and later is the version of OBIEE that is supported for migration to OAC. OBIEE stands for Oracle Business Intelligence Enterprise Edition, which is a comprehensive business intelligence platform that provides a range of analytics capabilities, such as interactive dashboards, ad hoc queries, enterprise reporting, and more. OAC stands for Oracle Analytics Cloud, which is a cloud-based analytics platform that provides similar features as OBIEE, as well as additional features such as data visualization, augmented analytics, machine learning, and more. You can migrate content from OBIEE to OAC using the Oracle BI Migration Tool, which is a command-line utility that allows you to export and import metadata and catalog objects between OBIEE and OAC. The Oracle BI Migration Tool supports migration from OBIEE 11.1.X and later versions to OAC, as these versions are compatible with OAC in terms of data model structure, catalog format, and security model. The other versions of OBIEE, such as OBIEE 12c, OBIEE 12g and later, and OBIEE 10g, are not supported for migration to OAC, as they are either not compatible with OAC or not available. References: Oracle Help Center, Oracle Help Center, Oracle Help Center

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

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]

To design an analytics solution that requires self-service analytics capabilities for product management and sales teams, as well as highly formatted reports delivered by scheduled email to external resellers, you must use OAC Enterprise Edition. OAC Enterprise Edition is the most comprehensive edition of Oracle Analytics Cloud that offers both self-service and governed analytics capabilities, as well as BI Publisher for creating pixel-perfect reports and delivering them by email or other channels. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

Using the explain feature and adding the relevant visualizations to a canvas are two methods that will help you build visualizations that will help your client understand their data better in Oracle Analytics Cloud. The explain feature is a feature that allows you to automatically generate insights and recommendations for your data based on various factors, such as Basic Facts, Key Drivers, Segments, and Anomalies. Basic Facts are summary statistics that describe your data, such as count, sum, average, minimum, and maximum. Key Drivers are variables that have a strong influence or correlation with your data, such as product category, customer segment, or region. Segments are groups of data points that have similar characteristics or patterns, such as high-value customers, low-performing products, or seasonal trends. Anomalies are data points that deviate significantly from the normal or expected behavior of your data, such as outliers, spikes, or drops. You can use the explain feature to quickly identify these factors and create visualizations that highlight them. You can also add the relevant visualizations to a canvas, which is a workspace where you can arrange and display your visualizations in a meaningful way. You can choose from various types of visualizations, such as charts, graphs, maps, tables, and more. You can also customize your visualizations by applying filters, calculations, aggregations, and other functions to your data. You can use these methods to create visualizations that will help your client understand their data better and discover new insights. References: [Oracle Help Center], [Oracle Help Center], [Oracle Help Center]

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]

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]

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

A scenario is a way to use a machine learning model within a canvas of an Oracle Analytics Cloud project. A scenario allows you to define inputs and outputs for the model, and then use them to create visualizations or calculations on the canvas. You can also compare different scenarios and see how changing the inputs affects the outputs. 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]