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| Section | Weight | Objectives |
|---|---|---|
| Data Layer | 25% | - Implement and configure the right data transformations
|
| Analytics Dashboard Design | 20% | - Design and build CRM Analytics dashboards
|
| Analytics Dashboard Configuration | 25% | - Configure dashboards
|
| Einstein Discovery | 20% | - Describe Einstein Discovery concepts
|
| Security | 10% | - Determine security and sharing settings
|
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NEW QUESTION # 16
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: B
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 # 17
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: A
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 # 18
A client has two datasets that are used across seven different dashboards. Three of these dashboards are used by marketing and four are used by sales. The client requires that only marketing can access the marketing dashboards and only sales can access the sales dashboards.
Which solution should a consultant recommend?
Answer: C
NEW QUESTION # 19
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: B
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 # 20
The CRM Analytics consultant at AW Computing is designing dashboard. They want to add a rate between two metrics and display it in a new chart. They are finding that the new chart seems to prejudice the design of other charts.
What should the consultant do to resolve this?
Answer: B
NEW QUESTION # 21
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