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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?
A CRM Analytics administrator is working on deploying a dataflow and a dataset (generated by this dataflow) to another org.
While creating a change set, they notice that the components are NOT visible to be included in the change set.
What is the reason for this?
A CRM Analytics consultant is reviewing results from an Einstein Discovery story with a business user. They agree with the findings but notice that none of the fields used in the story have a correlation value greater than 4%. The client is now concerned that the model
may not be good enough to deploy.
Which action should the consultant take?
What can a consultant accomplish in the Predictions page of a model?
In the Predictions page of a model within CRM Analytics (formerly Einstein Discovery), users have the capability to interact with the model to see how different input values affect predictions. Here's a detailed look at what can be accomplished:
Manual Input of Predictor Values: Users can manually input or change values for predictor fields to see how these changes alter the predicted outcomes. This is especially useful for testing hypothetical scenarios and understanding how sensitive the model is to various inputs.
Real-Time Interaction: This functionality provides a hands-on way to explore the model's behavior, offering immediate feedback on how input variations impact predictions, which can help in refining the model or in training users on its application.
Scenario Analysis: By adjusting predictor values, users can conduct scenario analyses, which are crucial for strategic planning and decision-making.
Cloud Kicks has informed CRM Analytics developers that they have two scenarios with restricted row-level security.
The parameters being:
1. Non-CXOs and VPs working in EMEA can have access to EMEA records only.
2. CXOs and VPs should have access to all data irrespective of the region (APAC, EMEA, etc.).
Which sharing method works for this scenario?
For Cloud Kicks' requirements regarding access to data based on roles and geographic regions, the most efficient and scalable approach is to implement row-level security using fields on the user record, like Department or Region. Here's the rationale for choosing this approach:
Scalability and Maintenance: By applying security rules based on user record fields, Cloud Kicks can manage access dynamically without needing to maintain multiple dashboards or datasets. This reduces administrative overhead and simplifies updates as roles or regional structures change.
Flexibility: Using a field on the user record to control access allows for easy expansion or modification of security policies as new regions or roles are added.
Simplicity: This method ensures a clear and straightforward security model that can be easily audited and understood by administrators and compliance teams.
