Cloud Kicks (CK) has a dashboard in CRM Analytics with forecasting dat
a. One widget is a compare table using the timeseries function showing the quarterly forecast. However, CK is interested in enhancing the dashboard with a weekly forecast per customer.
How should CK achieve this?
Answer : B
To create a weekly forecast per customer, an SAQL (Salesforce Analytics Query Language) query can be used. The timeseries function generates forecast data based on a specified date range. In this case, the forecast is customer-specific, meaning the partition should be based on Account Name to ensure that the forecast is generated for each customer. The date column (Date Cols) should be set to Y-M-W to aggregate the forecast data on a weekly basis.
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
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.
Universal Containers (UC) has a "Sales Manager'' dashboard. UC has a compare table that has multiple groupings and columns added showing the Total and Subtotals of the numeric values. A consultant is
asked to add additional groups to enhance details about UC's customers.
Which feature should the consultant use to make the navigation of the compare tables easier for the end user?
Answer : B
A CRM Analytics consultant has been asked to bring data from an external database as well as five external Salesforce environments into CRM Analytics. 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 : A
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.
After the initial creation of a model, the first model insight explains
93% of the variation of the outcome variable. This is unusually high.
What is the most likely reason for this?
Answer : C
A CRM Analytics consultant at Cloud Kicks is trying to upload data using an External Data API and the CSV file with the data was uploaded successfully. Upon analyzing the data using a lens, they find
they are unable to perform any mathematical operations as all the data and fields are treated as dimensions.
What is causing the problem?
Answer : A
Cloud Kicks needs a CRM Analytics consultant to install the Appointment Analytics App. After installation, they realize the wrong field was picked and the app did not have access to a newly created field that should be used instead of the old one.
What is the first step the consultant should take to prevent erroneous dataset/dashboard creation?
Answer : B
If the wrong field is selected in the initial setup of an app or dataset, it is important to stop any data processing activities (like recipe executions) to prevent erroneous data from being loaded into datasets and dashboards. In this case, stopping the recipe from running in Data Manager is the correct first step. Once the recipe is stopped, the consultant can update the field selection or make other necessary corrections before restarting the process.