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Universal Containers' CRM Analytics team is building a dashboard with two widgets.
1. List widget associated to the query "Type_2" and grouped by the dimension "Type" (multi-selection)
2. Donut chart widget associated to the query "Step_pie_3" and grouped by the dimension "Type"
The team wants to use bindings/interactions so any selection in the List widget will filter the Donut chart. The queries use different datasets,
and users should be able to choose more than one Type (multi-selection).
What is the right syntax for the binding/interaction?
A)
B)
C)
For the given requirement where a selection in a list widget needs to filter data displayed on a donut chart, and considering the list allows for multi-selection of the 'Type', the correct binding/interaction would be to use a syntax that captures the multi-select aspect and passes it appropriately. The right syntax, as indicated in Option A, looks like this:
'{{column(Type_2.selection, [\'Type\']).asObject()}}'
This syntax ensures:
Multi-selection: The column() function in combination with .asObject() ensures that multiple selected values from the 'Type_2' query can be passed as an object, which the donut chart can utilize to filter its content.
Correct Data Type Handling: By using .asObject(), the binding ensures the data passed between widgets maintains the correct structure expected by the CRM Analytics dashboard, thereby ensuring accurate filtering.
After the initial creation of a model, the first model insight explains
93% of the variation of the outcome variable. This is unusually high.
What is the most likely reason for this?
A customer has a dataset consisting of over 300 unique product names. They request a prediction model with the product names included.
Which action should the CRM Analytics consultant take?
Universal Containers asks a CRM Analytics consultant to review the performance of its local data sync.
After removing unused objects and fields from connected data, what else should the consultant do to improve performance of the data sync?
To improve the performance of local data sync in Universal Containers, evaluating the connection mode for each connected object is a practical approach. Here's the rationale:
Optimization of Resources: Different connection modes (e.g., Full Sync, Incremental Sync) use different amounts of resources. Choosing the right mode for each object based on how frequently its data changes can optimize the sync process and reduce load times.
Efficient Data Handling: By tailoring the connection mode to the needs of specific data objects, the overall efficiency of the data sync process is improved, leading to faster refresh rates and more timely data availability.
Cost and Performance Balance: Evaluating and selecting the appropriate connection mode can also help balance performance needs with cost constraints, as some modes may consume more compute resources than others.
A versioning feature allows CRM Analytics users to be added as Publishers and make changes separately while a 'Live' version is still being used by other users. Once the changes are complete, the user can then set their updated version as the Live version.
Which CRM Analytics item is this leveraged for?
In CRM Analytics, the versioning feature described is typically leveraged for Apps. This feature allows:
Parallel Development: Users can work on changes in a separate version without affecting the live version being accessed by others.
Controlled Publishing: Once changes are finalized, the user can then promote their version to be the new live version, ensuring seamless updates without disrupting ongoing usage.
Collaborative Workflows: Facilitates teamwork by allowing multiple users to propose and test changes in a controlled environment before making those changes live.
This approach ensures that CRM Analytics apps remain dynamic and can evolve over time while maintaining stability and continuity for end-users.
