Practice Free CRM Analytics and Einstein Discovery Consultant Exam Online Questions
A user is able to access the dashboards, lenses, and datasets of a particular app but is unable to
change the name of the specific app.
What is causing the issue?
- A . The user does not have Manager access for that app.
- B . The app name cannot be changed once created.
- C . The user does not have Editor access for that app.
A
Explanation:
In CRM Analytics, the ability to modify the name of an app or make other significant changes typically requires Manager access. This level of access is distinct from Editor or Viewer permissions, which may allow for modifications to contents within the app but not to the app’s core properties like its name. Here’s the reasoning:
Access Restrictions: Manager access is specifically designed to control structural changes within the app, including renaming the app, which is considered a higher privilege operation.
Role-Based Access Control: This ensures that only users with the necessary permissions can make significant changes, protecting the integrity and configuration of the app.
Ensuring users have the appropriate level of access based on their responsibilities is a fundamental aspect of managing security and functionality in CRM Analytics.
A CRM Analytics consultant is building a dashboard for Cloud Kicks that is embedded in a separate Lightning page called "Management Dashboard" using a CRM Analytics Dashboard Component. The system administrator and the contract manager should both have access. The system administrator is able to see the dashboard and the data, but the contract manager sees a blank Lightning page.
What is causing the issue?
- A . The consultant has set up component visibility for the dashboard for system administrators only.
- B . The consultant has set up a dashboard filter condition for data to be visible to system administrators only.
- C . The consultant has set up/enabled a ‘Hide on Error’ feature for the dashboard while embedding it.
A
Explanation:
When embedding a CRM Analytics dashboard in a Lightning page using a CRM Analytics Dashboard Component, you must configure the component’s visibility settings correctly to ensure that all relevant users have access. In this case, the issue arises because the system administrator can see the dashboard, but the contract manager cannot. The most likely cause is that the consultant has set the component visibility to display only for system administrators, which would prevent the contract manager from seeing the content. To resolve this issue, the consultant must modify the component visibility settings to include both the system administrator and contract manager profiles.
Reference: CRM Analytics and Lightning Components
Which statement best describes how to ensure CRM Analytics dashboards are easily used across both desktop and mobile devices?
- A . Create multiple layouts and reorder all the widgets so that they fit nicely within the new default width
- B . Create multiple layouts and allow CRM Analytics to automatically select and organize dashboard contents to be optimal for the device type.
- C . Create multiple layouts, ensure the layout selectors match the device, and resize/hide widgets to ensure the content is appropriate for the device screen size.
C
Explanation:
To ensure that CRM Analytics dashboards are optimally usable on both desktop and mobile devices, creating multiple layouts tailored to each device type is crucial. Here’s why Option C is the best approach:
Device-Specific Layouts: By creating specific layouts for each device type, you ensure that the dashboard contents are presented in a manner best suited to the screen size and interaction model of the device.
Layout Selectors: These are used to automatically display the appropriate layout based on the device accessing the dashboard, enhancing user experience without manual intervention.
Widget Customization: Resizing or hiding certain widgets for specific device layouts ensures that the dashboard remains clean, uncluttered, and easy to navigate, regardless of the device used.
A project team member uploads a CSV file to CRM Analytics, and they notice a few records failed during the upload. The manager wants to view the error log generated so this can be fixed and uploaded again. The manager has the CRM Analytics administrator permission but is unable to download the error log details.
Why is the manager unable to download the log details?
- A . They do not have the Upload External Data to CRM Analytics permission enabled.
- B . They do not have the Download CRM Analytics Data permission enabled.
- C . Only the user who uploaded the external data file can download the error log.
C
Explanation:
In CRM Analytics, when a CSV file is uploaded and errors occur during the upload process, an error log is generated. However, only the user who uploaded the external data file can download the error log, even if other users have administrative permissions. This restriction ensures that only the user responsible for the data upload can access the details to resolve the issues.
Reference: The limitations and permissions surrounding external data uploads and error logs are outlined in CRM Analytics permissions documentation.
A project team member uploads a CSV file to CRM Analytics, and they notice a few records failed during the upload. The manager wants to view the error log generated so this can be fixed and uploaded again. The manager has the CRM Analytics administrator permission but is unable to download the error log details.
Why is the manager unable to download the log details?
- A . They do not have the Upload External Data to CRM Analytics permission enabled.
- B . They do not have the Download CRM Analytics Data permission enabled.
- C . Only the user who uploaded the external data file can download the error log.
C
Explanation:
In CRM Analytics, when a CSV file is uploaded and errors occur during the upload process, an error log is generated. However, only the user who uploaded the external data file can download the error log, even if other users have administrative permissions. This restriction ensures that only the user responsible for the data upload can access the details to resolve the issues.
Reference: The limitations and permissions surrounding external data uploads and error logs are outlined in CRM Analytics permissions documentation.
A consultant is preparing a dataset to predict customer lifetime value and is collecting data from a questionnaire that asks for demographic information. A very small number of respondents fill in the Income box, but the consultant thinks that it is an informative column even though it only represents 1% of respondents.
What should the consultant do?
- A . Fill in the missing data with an average of all incomes.
- B . Apply the predict missing values transformation in recipe nodes.
- C . Drop the field as it will be difficult to get future respondents.
B
Explanation:
In CRM Analytics, when dealing with incomplete data, specifically when certain respondents have not filled out fields like income, the Predict Missing Values transformation in a recipe is highly effective. This transformation allows you to predict values for missing fields based on patterns from the existing data. Since the consultant finds this field informative despite having data from only 1% of respondents, applying this transformation can estimate these missing values, which ensures that the dataset remains useful for predictive purposes without discarding important variables.
Reference: CRM Analytics Recipes and Predict Missing Values
Universal Containers (UC) builds three Einstein Discovery models in Salesforce to predict and maximize its revenue per customer. The models are for every region UC has a business: EMEA, AMER, and APAC.
How should a consultant help UC deploy the three Einstein models to Salesforce?
- A . Filter the account data per region and deploy the same model to all segments.
- B . Segment the account data per region and deploy the appropriate model for each segment.
- C . Deploy the same mode! to all accounts and use an Apex trigger to segment the prediction.
B
Explanation:
In deploying Einstein Discovery models that are tailored to different regions (EMEA, AMER, and APAC), the best approach is to segment the account data by region and apply the specific model designed for each segment.
This method ensures the following:
Relevance and Accuracy: Each model can be specialized to understand and predict based on regional dynamics, which may differ significantly across geographies in terms of market behavior, customer preferences, and economic conditions.
Efficiency: Deploying region-specific models avoids the dilution of predictive power that might occur
if a single model were used across all regions, which could lead to less accurate predictions. Scalability: This approach is scalable as UC can further refine each model as more regional data becomes available or as regional market conditions evolve.