Oracle 1z0-1118-23 Dumps

JSON Web Token (JWT) is the recommended method of authentication by Oracle for creating the FAW instance. JWT is a secure and self-contained way of transmitting information between parties as a JSON object. It is used to assert some number of claims

The steps that you would take to override the default data security for specific users are to create a custom data role and set up data filters that specify the values for specific dimensions. A custom data role allows you to define custom data security filters and apply them to dimensions, facts, or subject areas. You can then assign the custom data role to the users who need to have different data access than the default2. You do not need to create a custom duty role or create reports with filters to override the default data security.

The smallest interval of incremental data load to the FAW data warehouse is daily. You can configure the incremental refresh frequency for each functional area in the pipeline settings. The minimum frequency that you can set is daily, which means that the data pipeline will run once every 24 hours and bring in the latest data from your Fusion Applications4. You cannot set the frequency to be hourly or weekly, as these are not supported by FAW.

To perform data validation from the FAW console, you need to ensure that you have the same data security privileges in the FAW instance and the Oracle Transactional Business Intelligence (OTBI) instance. This will allow you to compare the metrics between the two instances and verify their accuracy1. You do not need to have access to all data in the FAW instance or have the administrator privilege in the OTBI instance to perform data validation. 

The way that the Subject Areas are secured in FAW is by using duty roles. Duty roles define the functional security for a user, such as which tasks they can perform or which objects they can view. Duty roles also determine which subject areas a user can access based on their job functions1. There is a need to secure Subject Areas as the data security alone does not provide the necessary security access. Unlike Fusion Applications, the Subject Areas are not open to any FAW-licensed user, but only to those who have the appropriate duty roles

To test your customization, you need to publish the branch that contains your customization. This will apply the changes to the model and make them available in the subject areas. You can then use OAC console to create and view reports based on your customized model3

You can use FAW Semantic Model Extensions to customize the semantic model and join the Fusion Applications data with external data. You can extend ready-to-use dimensions with additional attributes from another data source, add a fact to an existing subject area, and reorganize the ready-to-use subject areas to create a new subject area to name a few

The way that you would secure your Fact table using data filters from different dimensions is to use functional groups to combine the data filters using the OR and AND operators. Functional groups are logical groups of dimensions that can be used to apply data filters on a Fact table. You can use functional groups to specify complex filter criteria that involve multiple dimensions and operators3. You do not need to create multi-table data sets in OAC or accept that this scenario is not supported.

The security privilege that is required by the user to activate UCC and FAW in your Oracle Cloud account is to be a Cloud Account Administrator in the Oracle Cloud account. The Cloud Account Administrator is responsible for activating UCC and FAW subscriptions, managing users and roles, configuring security settings, monitoring service usage, and performing other administrative tasks6. The user does not need to be a Business Administrator or have no special security privileges to activate UCC and FAW in the Oracle Cloud account.

Oracle Fusion Analytics Warehouse is built on top of Oracle Analytics Cloud and Oracle Autonomous Data Warehouse. This packaged service starts with Oracle Fusion Cloud Applications, and you can deploy rapidly, personalize, and extend it1

You cannot recover a terminated instance of FAW from the OCI console. When you terminate an instance, all the resources associated with it are permanently deleted, including the OAC instance, the ADW instance, and the data warehouse. You will have to re-create your instance and restore it from a snapshot if you have one7. You cannot recover a terminated instance by opening a Service Request (SR) with Oracle support or by clicking a button from the OCI console. 

 The way to ensure that your changes to the global pipeline parameters are deployed and data is consistent is to wait for the next incremental upload. Changes to the global pipeline parameters take effect at the next incremental upload, which is scheduled based on the frequency that you set in the parameters9. You do not need to reset the data warehouse or activate the functional areas for this purpose. Changes to the global pipeline parameters do not take effect immediately.

Based on a standard subscription, you can create two instances of Oracle Fusion Analytics Warehouse: one for development/testing, and a second for production use. You can use the development/testing instance to test new features or customizations before deploying them to the production instance3

 It is possible to set up SSO between Fusion Applications and FAW in this case, where you have already set up SSO for Fusion Applications between Active Directory and Fusion Applications. The configuration process to set up SSO between Fusion Applications and FAW stays the same, regardless of whether you use Active Directory or another IdP as your primary authentication source5. This configuration is supported and not complex or discouraged by Oracle. 

 You can create a dimension using Semantic Model Extensions and join it with any Fact table, whether it is a custom Fact table or an out-of-the-box Fact table. You can also join multiple dimensions with a single Fact table3

Data Augmentation is a feature in FAW that supports both data movement and semantic model extensibility to provide missing data elements. Data Augmentation allows you to extract data from additional data sources that are not part of the FAW data pipeline and load them into ADW. You can then use the data to customize the semantic model and create new dimensions, facts, and subject areas2