The Salesforce Certified CRM Analytics and Einstein Discovery Consultant exam validates your ability to design, implement, and optimize analytics solutions within the Salesforce ecosystem. This certification is ideal for consultants, architects, and administrators who lead CRM analytics initiatives and leverage Einstein Discovery to drive business insights. This page provides a structured study roadmap, covers core exam topics, and highlights preparation strategies to help you succeed on the CRM-Analytics-and-Einstein-Discovery-Consultant exam.
Use this topic map to guide your study for the Salesforce Certified CRM Analytics and Einstein Discovery Consultant certification within the Salesforce Consultant, CRM Analytics and Einstein Discovery Consultant path.
The exam uses multiple question types to assess both conceptual knowledge and practical problem-solving ability in real-world analytics scenarios.
Questions progress in difficulty and emphasize practical application, ensuring candidates can not only understand concepts but also implement them in production Salesforce environments.
A structured study plan aligned to exam topics helps you build confidence and retain knowledge efficiently. Dedicate time each week to specific domains, practice with realistic questions, and connect concepts across data modeling, security, configuration, and dashboard workflows.
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Analytics Dashboard Design, Analytics Dashboard Implementation, and Data Layer typically account for a significant portion of exam questions. However, all six domains are important, and security and configuration questions appear regularly. Focus on dashboard design and implementation first, then ensure solid coverage of data modeling and security principles.
In practice, these three areas form the foundation of any analytics solution. You model and prepare data in the Data Layer, enforce access controls through Security rules to protect that data, and then manage the overall system through Admin/Configuration. For example, after creating a data model, you must configure field-level security to hide sensitive fields and set up sharing rules so users see only relevant data. Understanding these connections helps you design end-to-end solutions.
Building at least one complete analytics project from start to finish is ideal. Prioritize hands-on labs that cover data connector setup, dashboard creation with filters and interactions, and Einstein Discovery project configuration. If time is limited, focus on dashboard implementation and data modeling labs, as these skills are tested most frequently and require practical familiarity with the CRM Analytics interface.
Many candidates overlook security implications when designing dashboards, forgetting that row-level security and field-level security affect what data users can see. Others underestimate the importance of data quality and modeling decisions, which impact all downstream analytics work. Additionally, some fail to consider performance and scalability when building dashboards with large datasets. Review security best practices and data model design patterns carefully.
Dedicate the first few days to reviewing weak topic areas identified in practice tests. Mid-week, take a full-length timed mock exam and analyze results in detail. In the final 2-3 days, do quick reviews of high-weight topics like dashboard implementation and Einstein Discovery, and re-read explanations for questions you answered incorrectly. Avoid cramming new material; instead, reinforce what you already know.
Universal Containers has a well-defined role hierarchy in Salesforce where everyone is assigned to an appropriate node. The accounts within their instance are categorized by their demography.
An individual sales rep should be able to view all accounts that they own. In addition, sales reps should be able to see any accounts where the value of the account demography matches the demography defined on their user record. A user could have more than one demography defined on their user record.
To meet this requirement, the CRM Analytics consultant has set up a security predicate of the existing 'Account' dataset as follows:
This, however, does not seem to be working as expected.
What is causing the issue?
The issue with the security predicate not functioning as expected likely stems from a permissions issue related to the custom field Demographic__c on the User object. Here's a detailed explanation:
Field-Level Security: If the sales reps do not have access to the Demographic__c field, the security predicate which references this field cannot execute properly as the system cannot evaluate the predicate without accessing the field.
Permission Settings: Ensuring that the sales reps have the necessary permissions to view and use the Demographic__c field is crucial for the security predicate to function correctly.
Data Visibility: The security model in CRM Analytics relies heavily on the underlying data permissions in Salesforce. If these permissions are not correctly configured, the expected data visibility through CRM Analytics will not be achieved.
Universal Containers' sales team is looking to build a dashboard that shows the total revenue from their top 10 accounts (based on revenue) and make it dynamic with the filters of the dashboard.
Which action is required to accomplish this?
To create a dashboard showing the top 10 accounts by revenue dynamically, the best approach is to create a query that sorts the accounts in descending order by revenue and limits the results to the top 10. This query can then be used as the data source for the widget, ensuring that it dynamically adjusts based on the filters applied to the dashboard.
This method efficiently handles the task because sorting by revenue and limiting the results to 10 ensures that only the top accounts are displayed, and it remains dynamic with dashboard filters.
Universal Containers has a dashboard for sales managers that want to visualize their win rate.
Which chart type should the consultant use to keep track of targets?
A consultant creates a CRM Analytics dashboard in a sandbox and it needs to be migrated into production.
What should the consultant use to complete the migration?
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?
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.
