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| Section | Weight | Objectives |
|---|---|---|
| Einstein Discovery | 20% | - Implement Einstein Discovery
|
| Security | 10% | - Determine security and sharing settings
|
| Analytics Dashboard Design | 20% | - Design and build CRM Analytics dashboards
|
| Analytics Dashboard Configuration | 25% | - Configure dashboards
|
| Data Layer | 25% | - Identify the right data sources and data flows for CRM Analytics
|
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NEW QUESTION # 34
A CRM Analytics consultant is adding direct data into an existing recipe. When trying to click on the input node, data preview is taking a long time to show any results.
How should the consultant resolve this?
Answer: C
NEW QUESTION # 35 
Which timeseries statement will fix the query?
Answer: A
NEW QUESTION # 36
A CRM Analytics consultant has been asked to bring data from an external database as well as five external Salesforce environments into CRMAnalytics. Twenty-five objects have been enabled from the local Salesforce connector.
The requirements are:
* 10 objects should be enabled from an external database
* 12 objects each from three of the external Salesforce environments
* 15 objects each from the remaining two external Salesforce environments The consultant estimates each connector will, per object, bring between 1,000 and 1 million rows of data.
Which limit will be exceeded?
Answer: B
Explanation:
In evaluating the scenario presented where multiple external sources and objects are being integrated into CRM Analytics, we need to consider the total number of enabled objects across all connections. Here's a breakdown:
* 10 objects from an external database
* 12 objects each from three external Salesforce environments, totaling 36 objects
* 15 objects each from two external Salesforce environments, totaling 30 objects
* 25 objects already enabled from the local Salesforce connector
This brings us to a total of 101 objects enabled, which may exceed typical limits on the number of objects that can be enabled in a CRM Analytics environment, depending on the specific Salesforce licensing and platform limits.
NEW QUESTION # 37
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 # 38
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?
Answer: C
Explanation:
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.
NEW QUESTION # 39
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