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Salesforce Building Lenses, Dashboards, and Apps in Tableau CRM Sample Questions (Q105-Q110):

NEW QUESTION # 105
A consultant runs the sharing inheritance coverage assessment for the Opportunity object and finds that some records exceed 400 sharing descriptors.
What should the consultant do?

A. Increase the sharing descriptor limit in the analytics settings.
B. Contact Salesforce Support to increase the sharing descriptor limit.
C. Use security predicates in CRM Analytics.

Answer: C

Explanation:
When a record exceeds 400 sharing descriptors, it can cause performance issues or sharing rule complications in CRM Analytics. In such cases, the recommended solution is to use security predicates, which allow fine-tuned control over which data is visible to users based on their sharing rules and permissions. Security predicates reduce the number of sharing descriptors by enforcing security at the dataset level rather than relying solely on record-sharing mechanisms.
Increasing the sharing descriptor limit is not an available option, and Salesforce Support does not typically increase this limit, making the use of security predicates the best approach.

NEW QUESTION # 106
A story created has an AUC of .62.
What two things could be done to possible improve this AUC score?
Choose 2 answers

A. Run detect sentiment on the dataflow before creating the story.
B. Add more rows of relevant data.
C. Segment the training set.
D. Change the order of the story insights to descending.

Answer: A,D

NEW QUESTION # 107
Universal Containers (UC) is using CRM Analytics to create two datasets.
* Dataset A: Contains a list of activities with an "activityID" dimension and a "userID" dimension
* Dataset B: Contains a list of users with a "userID" dimension
UC wants to delete all activities from Dataset A related to users in Dataset B.
How should the CRM Analytics consultant help UC achieve this?

A. Use the recipe node "delete" and set "userID" as the deletion ID.
B. Use the combination of recipe transformations: "join" and "filter".
C. Use the "update" transformation and utilize the "filter" node.

Answer: B

Explanation:
In CRM Analytics, when dealing with two datasets, such as Dataset A (activities) and Dataset B (users), and you want to delete records from Dataset A based on the users listed in Dataset B, you would typically use a combination of a join and filter transformation in a recipe. The join transformation allows you to combine data from both datasets based on the shared userID dimension, and the filter transformation would then be used to delete or exclude any activities from Dataset A that are associated with the users from Dataset B. This approach ensures that only relevant activities remain in Dataset A after filtering out the unwanted ones.
Reference: CRM Analytics Recipes: Join and Filter Transformations

NEW QUESTION # 108
Philip adds a recently created Seed Bank Orders dataset to an Einstein Analytics app for which the Mosaic Seed Bank project team has the Viewer app role. How much data in the Seed Bank Orders dataset can a project team member see?

A. Only rows designated by the App Manager
B. None of the data
C. Only rows designated by the Salesforce administrator
D. All rows and fields in the datasets

Answer: D

NEW QUESTION # 109
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?

A. Append
B. Update
C. Overwrite

Answer: C

Explanation:
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.

NEW QUESTION # 110
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