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Salesforce Analytics-Con-201 Exam Syllabus Topics:

SectionWeightObjectives
Einstein Discovery20%- Implement Einstein Discovery
  • 1. Configure and deploy Einstein Discovery models
  • 2. Integrate predictions into CRM Analytics dashboards
- Describe Einstein Discovery concepts
  • 1. Explain the model building process
  • 2. Identify the use cases for Einstein Discovery
  • 3. Interpret model results
Analytics Dashboard Design20%- Design and build CRM Analytics dashboards
  • 1. Use Lenses and Dashboards
  • 2. Describe the use cases for Visualforce and Lightning components
  • 3. Apply design best practices
Security10%- Determine security and sharing settings
  • 1. Describe how Salesforce security is applied in CRM Analytics
  • 2. Implement row-level security
Analytics Dashboard Configuration25%- Configure dashboards
  • 1. Set up dynamic dashboards
  • 2. Configure bindings
  • 3. Apply query filters
Data Layer25%- Implement and configure the right data transformations
  • 1. Use Data Prep actions
  • 2. Identify and resolve data sync issues
- Identify the right data sources and data flows for CRM Analytics
  • 1. Describe how Salesforce objects map to CRM Analytics
  • 2. Describe use cases for Data Prep (Dataflows and Recipes)
  • 3. Define datasets

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Analytics-Con-201 Prüfungsfragen mit Lösungen (Q75-Q80):

75. Frage
What can a consultant accomplish in the Predictions page of a model?

Antwort: C

Begründung:
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.


76. Frage
A CRM Analytics consultant at Cloud Kicks is trying to upload data using an External Data API and the CSV file with the data wasuploaded successfully. Upon analyzing the data using a lens, they find they are unable to perform any mathematical operations as all the data and fields are treated as dimensions.
What is causing the problem?

Antwort: C


77. 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.


78. Frage
Universal Containers plans to upload target data from an external tool to CRM Analytics so it can calculate the sales team target attainments.
The target data changes every month, so the datasets need to be updated on a monthly basis. The target data is a CSV file that contains the Salesforce ID of the sales rep, the target amount, and the month of the target. For each sales rep, the file contains a target for every month of the current year as well as all previous years.
Based on this information, which operation should a consultant use with the Analytics External Data API to upload the file?

Antwort: C

Begründung:
For uploading target data that changes on a monthly basis and includes historical data (previous years' targets), the appropriate operation is "Overwrite." This ensures that each time the CSV file is uploaded, the existing data in the dataset is replaced with the new data. This is critical because the target data includes both current and historical data, and using "Overwrite" will update the entire dataset while maintaining historical accuracy.
"Append" would add new data without replacing the old records, leading to duplication, and "Update" is not suitable for completely replacing data in this context.
Reference: The usage of the "Overwrite" operation for updating datasets with changing data is covered in theAnalytics External Data APIdocumentation, as well as in theWave Analytics Explorermodule.


79. Frage
The administrator at Cloud Kicks has been asked to sync data from an external object created in Salesforce into CRM Analytics.
What should the administrator keep in mind?

Antwort: B


80. Frage
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