Oracle 1z0-1041-23 Oracle Cloud Infrastructure 2023 Enterprise Analytics Professional Exam Practice Test

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]

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]

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]

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 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. 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

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]

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]

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]

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]

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

Two filters that can be applied for Region and Sales Amount are:

• Range filter for Region and Date filter for sales Amount. A range filter allows you to specify a range of values for a column using operators such as greater than, less than, between, or not between. A date filter allows you to specify a date or a date range for a column using operators such as before, after, on, or between. For example, you can use a range filter to select only the South region and a date filter to select only the months with sales amount more than 1 million USD.
• List filter for Region and List filter for Sales Amount. A list filter allows you to select one or more values from a list of values for a column using operators such as equals, not equals, in, or not in. For example, you can use a list filter to select only the South region and another list filter to select only the sales amounts that are more than 1 million USD. References: [Oracle Analytics Cloud - Data Visualization User’s Guide], [Oracle Analytics Cloud - Data Visualization User’s Guide]

The input containing a comma (,) in the search text is a possible problem that could prevent you from getting any results when searching for saved objects in Oracle Analytics Cloud. The comma is a special character that is used to separate multiple search terms or phrases. If you use a comma in your search text, you need to enclose the text in double quotation marks (“”) to indicate that it is a single term or phrase. For example, if you want to search for an object named “Sales, Revenue, and Profit”, you need to enter it as ““Sales, Revenue, and Profit”” in the search box. The other options are not problems with the input, as they are valid ways to search for saved objects. You can use an asterisk () as a wildcard character to match any number of characters in your search text (for example, "revenue"). You can use the object name only as search text, as this will return all objects that match or contain the name. You can use the date attribute in the year-month-day format, as this will return all objects that were created or modified on that date. 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]

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]

The step that is required in a data flow to create a new curated data set is Add Data. Add Data is the first step in any data flow that allows you to select one or more data sources to use in your data flow. You can choose from various types of data sources, such as databases, files, applications, or existing data sets. You can also specify how to join or union multiple data sources if needed. 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]

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]

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