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| Section | Weight | Objectives |
|---|---|---|
| Analytics Dashboard Configuration | 25% | - Configure dashboards
|
| Analytics Dashboard Design | 20% | - Design and build CRM Analytics dashboards
|
| Security | 10% | - Determine security and sharing settings
|
| Einstein Discovery | 20% | - Implement Einstein Discovery
|
| Data Layer | 25% | - Identify the right data sources and data flows for CRM Analytics
|
>> Detail Analytics-Con-201 Explanation <<
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NEW QUESTION # 44
A new picklist value was added for the Category field on the Account object. This field is already added as part of the Account object data sync and the respective recipe that uses this field.
The CRM Analytics team reports that when they start the recipe it runs successfully with no errors or warnings, but theyare unable to See this new valueson their existing dashboards.
What is the oridi4 of this issue?
Answer: C
NEW QUESTION # 45
An CRM Analytics consultant is working with Ursa Major Solar to build a dashboard to understand customer renewals. Each subscription is captured as a Closed Won Opportunity within Salesforce and asingle Account should only have one active subscription. The consultant notices the Opportunity record does NOT specify whether it is a renewal or a net new subscription.
Which data transformation should the consultant use to determine if a subscription is new or a renewal?
Answer: A
Explanation:
To determine whether a subscription is new or a renewal from the Opportunity records in Salesforce, the consultant should utilize a Custom Formula in the data transformation process. Here's the rationale:
* Custom Formula Usage: By employing a custom formula, the consultant can create a logical expression that checks the historical data associated with each account. If an account has previous closed-won opportunities, any new opportunities can be labeled as renewals; otherwise, they are considered new subscriptions.
* Data Insight: This method provides a straightforward way to derive new insights (new vs. renewal) directly from existing data without altering the data structure itself, making it a non-invasive and efficient solution.
* Implementation: The custom formula can be applied in a recipe or directly within a dataflow in CRM Analytics, offering flexibility in how and where the transformation is executed.
NEW QUESTION # 46
An CRM Analytics consultant creates a model to maximize the satisfaction of cases. They want to deploy the mode! and further use the model and predictions in an existing automated business process powered by Salesforce Flow.
What is the most efficient way to accomplish this?
Answer: C
NEW QUESTION # 47
A CRM Analytics consultant is asked to make sure the new sales performance dashboard is accessible in the mobile app. They have created multiple mobile layouts for the same dashboard.
How is CRM Analytics expected to perform?
Answer: B
NEW QUESTION # 48
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
Answer: C
Explanation:
In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:
* Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.
* Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors- essentially, the average squared difference between the estimated values and what is estimated.
These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model's predictions in real-world applications.
NEW QUESTION # 49
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