Most Recent Oracle 1Z0-1041-23 Exam Dumps

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]

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

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

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

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. Reference:Oracle Help Center,Oracle Help Center