Salesforce CRM Analytics and Einstein Discovery Consultant Practice Exams
Last updated on Oct 22,2026- Exam Code: CRM Analytics and Einstein Discovery Consultant
- Exam Name: Salesforce Certified CRM Analytics and Einstein Discovery Consultant Exam
- Certification Provider: Salesforce
- Latest update: Oct 22,2026
Universal Containers plans to upload target data from an external tool to CRM Analytics so it can calculate the sales team target attainments.
The target data changes every month, so the datasets need to be updated on a monthly basis. The target data is a CSV file that contains the Salesforce ID of the sales rep, the target amount, and the month of the target. For each sales rep, the file contains a target for every month of the current year as well as all previous years.
Based on this information, which operation should a consultant use with the Analytics External Data API to upload the file?
- A . Overwrite
- B . Update
- C . Append
A
Explanation:
For uploading target data that changes on a monthly basis and includes historical data (previous years’ targets), the appropriate operation is "Overwrite." This ensures that each time the CSV file is uploaded, the existing data in the dataset is replaced with the new data. This is critical because the target data includes both current and historical data, and using "Overwrite" will update the entire dataset while maintaining historical accuracy.
"Append" would add new data without replacing the old records, leading to duplication, and "Update" is not suitable for completely replacing data in this context.
Reference: The usage of the "Overwrite" operation for updating datasets with changing data is covered in the Analytics External Data API documentation, as well as in the Wave Analytics Explorer module.
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?
- A . JSON metadata file was not uploaded along with the CSV data file.
- B . The field value added in the CSV file was contained within double quotes.
- C . Proper transformations need to be performed at the external source prior to External Data API callout.
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 they are unable to See this new values on their existing dashboards.
What is the oridi4 of this issue?
- A . The user who runs the dataflow/recipe does not have access to the field.
- B . The Integration User profile does not have access to the field.
- C . There are no records in Salesforce with this new plicklist value.
A team of CRM Analytics developers has been working on an existing recipe to add new derived fields. The edited version has been failing ever since, and management is requesting that the dashboard show refreshed data while they work on the edits.
How can the developers add new fields while keeping the dataset refreshed?
- A . A recipe for the new fields and when that is successful, add it to the existing recipe as a join node.
- B . Clone the existing recipe to add fields and roll back the original recipe to the last working version.
- C . Refresh the dataset after working hours to avoid the edited version from failing.
B
Explanation:
When faced with the need to continue refreshing data while developing new features in a recipe, the best practice is:
Clone the Existing Recipe: By cloning the recipe, developers can experiment with adding new fields and transformations without affecting the production data flow. This allows for testing and development in a sandbox-like environment.
Roll Back to a Stable Version: Rolling back the original recipe to the last stable version ensures that the production dashboards continue to receive refreshed data, maintaining business operations without disruption.
This approach not only ensures data continuity but also provides a safe environment to address any
issues that may arise from new developments.
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.
A CRM Analytics administrator is working on deploying a dashboard and a dataset from a developer sandbox to a full sandbox. They have deployed the dataset via change set and manually copy-pasted the dashboard JSON into the target org. However, they notice that the conditional formatting and the widget-specific number formats have been lost in the target environment.
What is causing this issue?
- A . Analytics Dataset XMD was NOT included as part of the deployment package,
- B . The recipe that generated the dataset also needs to be included as part of the package.
- C . Analytics Dashboard XMD was NOT included as part of the deployment package.
A
Explanation:
When deploying a dataset and dashboard between environments in CRM Analytics, it’s essential to include the Extended Metadata (XMD) file, which controls aspects such as conditional formatting and number formatting. In this case, the administrator manually copied the dashboard JSON but did not
deploy the Analytics Dataset XMD, which leads to the loss of conditional formatting and widget-specific number formats in the target environment. Including the XMD ensures that all formatting and metadata are transferred correctly.
Reference: Detailed information on XMD files and their role in formatting is covered in the CRM Analytics deployment and customization documentation.
The marketing team at Cloud Kicks has five dashboards in an app. Four widgets are replicas of each other in three of the dashboards.
What is the best way to maintain these widgets?
- A . Create/Edit a lens and add each dashboard.
- B . Create/Edit the widgets individually on each dashboard.
- C . Create/Edit a component for the widgets.
C
Explanation:
To maintain consistency and ease of updates across multiple dashboards, creating or editing a component for the widgets is the most effective method.
This approach:
Efficiency in Updates: Allows changes to be made in one place, which automatically propagates to all instances where the component is used across dashboards.
Consistency: Ensures uniformity in the appearance and functionality of the widgets across different dashboards.
Simplicity: Reduces the need for redundant work, where each widget would otherwise need to be updated individually.
A CRM Analytics consultant is performing column profiling on dimension column in a recipe. Newly-added rows are not being considered in the Results tab of the profile even though a sync was run for that specific object.
What is causing the issue?
- A . The sample does not include changes to the connected object data within the last 24 hours.
- B . Sync operation has not run properly with the new dimension column in the recipe.
- C . Column profiling Is not applicable on a dimension column in a recipe.
A CRM Analytics consultant is working on Sales dashboards with multiple datasets and advanced queries in the Sales Analytics app.
Sales managers in the organization have been given Editor/Manager access to the app, whereas sales reps have been given Viewer access.
Some dashboards that are in progress are not ready to be rolled out to sales reps and should only be viewable by sales managers.
How should the consultant accomplish this?
- A . Remove the dashboard from the ‘Run App’ navigation list so the sales reps cannot navigate to these dashboards.
- B . Duplicate the dashboards and their respective datasets, and move the assets to a separate app for the sales rep.
- C . Leverage the CRM Analytics asset visibility feature to hide the assets from the users.
C
Explanation:
In CRM Analytics, you can control the visibility of dashboards and other assets using the asset visibility feature. This allows the consultant to restrict access to specific assets (like dashboards) for certain groups of users, such as sales reps, without needing to duplicate datasets or move dashboards to another app. This is the most efficient way to manage access for dashboards in progress while allowing only sales managers to view the in-progress dashboards.
Reference: The CRM Analytics asset visibility feature is described in detail in the Building Lenses,
Dashboards, and Apps module, where different user access levels are explained.
Universal Containers uses CRM Analytics to build dashboards for different departments: Sales, Service, and Marketing. Users in the same department have the same role and need to have access to the same dashboards. Dashboards for different departments use some common datasets with the same row-level security.
How should a CRM Analytics consultant address this need?
- A . Create one app for each department, put common datasets in the shared app, and use roles to share apps.
- B . Create one app for each department, put common datasets in the shared app, and use profiles to share apps.
- C . Create one app for each department, put common datasets in the shared app, and use permission sets to share apps.
C
Explanation:
For managing access to department-specific dashboards while leveraging common datasets, the best approach involves the use of apps and permission sets.
Here’s why:
App Segregation: Creating a separate app for each department (Sales, Service, Marketing) allows for tailored dashboards and datasets to be grouped by department, facilitating easier management and navigation.
Shared Common Datasets: Placing common datasets in a shared app ensures that all departments can access necessary data without duplication, maintaining consistency and reducing storage requirements.
Use of Permission Sets: Leveraging permission sets to control access to these apps is a flexible and scalable approach. Permission sets can be finely tuned to grant or restrict access based on user roles within the organization, and they can be easily adjusted as roles or organizational structures change. This structure not only ensures data security and appropriate access but also enhances the efficiency of managing CRM Analytics resources across different departments.