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| Section | Weight | Objectives |
|---|---|---|
| Analytics Dashboard Design | 20% | - Design and build CRM Analytics dashboards
|
| Security | 10% | - Determine security and sharing settings
|
| Data Layer | 25% | - Identify the right data sources and data flows for CRM Analytics
|
| Einstein Discovery | 20% | - Describe Einstein Discovery concepts
|
| Analytics Dashboard Configuration | 25% | - Configure dashboards
|
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NEW QUESTION # 102
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?
Answer: C
NEW QUESTION # 103
For a story created using multiple data sources (Salesforce, CSV, and external data), what is a possible fix for the issue shown in the prediction?
Answer: A
Explanation:
A supplemental dataset is appropriate when an Einstein Discovery model requires predictor information that is not available directly on the Salesforce object used for scoring. During model deployment, CRM Analytics allows supplemental datasets to provide external data, computed fields, or other required model variables. The supplemental dataset is associated with the Salesforce object through corresponding record-ID fields, allowing the additional predictor values to be supplied when predictions are generated. This directly addresses the missing-field situation shown in the exhibit. Deploying without connecting to a Salesforce object is intended primarily when the model does not depend on a Salesforce object, while actionable variables determine which variables Einstein can recommend changing to improve an outcome; they do not supply missing predictor data. Therefore, creating and associating a supplemental dataset is the appropriate solution.
NEW QUESTION # 104
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?
Answer: C
Explanation:
Reference:CRM Analytics Recipes: Join and Filter Transformations
NEW QUESTION # 105
A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "*&**(&*(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?
Answer: A
Explanation:
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules.
Headers containing non-alphanumeric characters, such as "&**(&(%", will automatically be adjusted.
Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with "X" to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.
NEW QUESTION # 106
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?
Answer: A
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 # 107
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