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| Section | Objectives |
|---|---|
| Einstein Discovery | - Model building and predictions - Insights interpretation and recommendations |
| Data Management and Preparation | - Dataset creation and configuration in CRM Analytics - Data transformation and dataflows |
| Data Integration and Connectivity | - Connecting external data sources - Data synchronization and refresh strategies |
| Dashboard and App Development | - Interactive filters and user experience optimization - Lens creation and dashboard design |
| Security and Access | - Row-level security and sharing rules - App and dataset permissions |
>> Analytics-Con-201 Unterlage <<
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112. Frage
Universal Containers has a dashboard for sales managers. They need the ability to visualize the number of Closed Won opportunities by month, quarter, or year, and then display the result in a single chart. A CRM Analytics consultant creates a custom query to display three values:
ClosedDate_month, ClosedDate_ quarter, and ClosedDate_year.
What should the consultant do next?
Antwort: B
113. Frage
Acompany wants to create a timeline chart to visualize the evolution of its Closed Won opportunities.
What are the required parameters to build a lens that displays output similar to the image shown?
Antwort: B
Begründung:
To create a timeline chart similar to the one shown, the following parameters are typically required:
* 1 Measure: This could be the count of Closed Won opportunities or any other relevant metric that needs to be tracked over time.
* 1 Grouping by a Date Field: This is essential to plot the timeline effectively. The date field would typically be the close date of the opportunities.
* Additional Groupings: Depending on the complexity and the detail needed, additional groupings can be added. For example, grouping by region or product line can provide more insights into the timeline. If trellis is used, it allows for the creation of multiple smaller charts within the main chart, each representing a slice of data based on the additional groupings.
This setup helps visualize the evolution of Closed Won opportunities over time, making it easy to spot trends, seasonal patterns, or other relevant insights.
114. Frage
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?
Antwort: B
Begründung:
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.
115. Frage
Which recommended technique should a CRM Analytics consultant
use to access CRM Analytics data from a remote app or website?
Antwort: B
116. Frage
What is a benefit of introducing a second local connector?
Antwort: B
Begründung:
Introducing a second local connector in CRM Analytics can improve performance by enabling more granular control over data syncs. By having a separate connector, different datasets or recipes can be synchronized independently based on specific refresh needs, reducing load and improving overall performance. This approach helps optimize data flow operations, especially in large-scale deployments with varying data refresh requirements.
117. Frage
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