Most Recent Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Exam Questions & Answers

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.

For Cloud Kicks' requirement to refresh dashboard data synchronously with the sync of an external connection to Google BigQuery, the most efficient method is:

Event-Based Triggers: Using CRM Analytics, you can set up recipes to run on an event-based trigger. This ensures that the recipe (which processes the data for the dashboard) only runs after the external data sync is completed.

Salesforce External Connection Syncs: By specifically targeting the synchronization event of the Salesforce external connection with Google BigQuery, the recipe ensures data consistency and timeliness without manual intervention.

Automation and Efficiency: This approach minimizes latency and maximizes data freshness, aligning data processing closely with data availability, thus enhancing dashboard accuracy.

For the given requirement where a selection in a list widget needs to filter data displayed on a donut chart, and considering the list allows for multi-selection of the 'Type', the correct binding/interaction would be to use a syntax that captures the multi-select aspect and passes it appropriately. The right syntax, as indicated in Option A, looks like this:

'{{column(Type_2.selection, [\'Type\']).asObject()}}'

This syntax ensures:

Multi-selection: The column() function in combination with .asObject() ensures that multiple selected values from the 'Type_2' query can be passed as an object, which the donut chart can utilize to filter its content.

Correct Data Type Handling: By using .asObject(), the binding ensures the data passed between widgets maintains the correct structure expected by the CRM Analytics dashboard, thereby ensuring accurate filtering.