A CRM Analytics consultant has been asked to bring data from an external database as well as five external Salesforce environments into CRM Analytics. Twenty-five objects have been enabled from the local Salesforce connector.
The requirements are:
* 10 objects should be enabled from an external database
* 12 objects each from three of the external Salesforce environments
* 15 objects each from the remaining two external Salesforce environments
The consultant estimates each connector will, per object, bring between 1,000 and 1 million rows of data.
Which limit will be exceeded?
In evaluating the scenario presented where multiple external sources and objects are being integrated into CRM Analytics, we need to consider the total number of enabled objects across all connections. Here's a breakdown:
10 objects from an external database
12 objects each from three external Salesforce environments, totaling 36 objects
15 objects each from two external Salesforce environments, totaling 30 objects
25 objects already enabled from the local Salesforce connector
This brings us to a total of 101 objects enabled, which may exceed typical limits on the number of objects that can be enabled in a CRM Analytics environment, depending on the specific Salesforce licensing and platform limits.
Which capability can a consultant use if ''Deploy without connecting to a Salesforce Object'' is selected while deploying the model?
When deploying a model with the option 'Deploy without connecting to a Salesforce Object', the suitable capabilities include:
Use of Predict Function in Salesforce Flows: This capability allows the deployed model to be used within Salesforce Flow as a predictive tool, enabling automation flows to include predictions without directly writing back to Salesforce objects.
Flexibility in Application: This method provides flexibility in how predictions are utilized across various Salesforce processes and workflows, without the need for direct data manipulation within Salesforce objects.
Enhanced Workflow Integration: By integrating predictive insights directly into flows, organizations can automate decision-making processes, enhance user interactions, and streamline operations based on predictive outcomes.
This setup aligns with Salesforce's best practices for leveraging CRM Analytics to enhance operational efficiency and decision accuracy across different business functions.
A consultant creates a CRM Analytics dashboard in a sandbox and it needs to be migrated into production.
What should the consultant use to complete the migration?
A team of CRM Analytics developers has been working on an existing recipe to add new derived fields. The edited version has been failing ever since, and management is requesting that the dashboard show refreshed data while they work on the edits.
How can the developers add new fields while keeping the dataset refreshed?
When faced with the need to continue refreshing data while developing new features in a recipe, the best practice is:
Clone the Existing Recipe: By cloning the recipe, developers can experiment with adding new fields and transformations without affecting the production data flow. This allows for testing and development in a sandbox-like environment.
Roll Back to a Stable Version: Rolling back the original recipe to the last stable version ensures that the production dashboards continue to receive refreshed data, maintaining business operations without disruption.
This approach not only ensures data continuity but also provides a safe environment to address any issues that may arise from new developments.
A CRM Analytics consultant is adding direct data into an existing recipe. When trying to click on the input node, data preview is taking a long time to show any results.
How should the consultant resolve this?
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