CRM Analytics and Einstein Discovery Consultant Practice Exam: Test Your Knowledge 2025

A CRM Analytics consultant needs to bring data from Salesforce objects and an external database into a single dataset for analysis. The external data updates daily via CSV file upload. What is the recommended approach to combine these data sources?

A dashboard designer wants to enable users to explore data interactively by clicking on chart elements to filter other widgets on the same page. Which feature should be implemented?

An organization wants to predict customer churn using Einstein Discovery. They have historical data with a 'Churned' field (Yes/No) and various customer attributes. What type of model should be created?

A consultant needs to ensure that sales representatives can only see analytics data for accounts in their assigned territory. The territory assignment is managed through Salesforce role hierarchy. What is the most appropriate security approach?

When creating a recipe, a consultant needs to add a field that calculates the number of days between the opportunity close date and today's date. Which transformation should be used?

A dashboard displays sales data with a date filter. Users want to compare current quarter performance to the previous quarter within the same visualization. What is the best approach to implement this?

After creating an Einstein Discovery model, a consultant needs to deploy it so predictions can be written back to Salesforce records. What must be configured to enable this functionality?

A dataflow extracts data from the Opportunity object and takes several hours to run, impacting dashboard refresh times. The dashboard only needs to show opportunities from the current fiscal year. How should the consultant optimize the dataflow performance?

A consultant is building a dashboard and needs to display KPIs with conditional formatting where values above target show in green and below target show in red. What feature should be used?

An Einstein Discovery story shows that 'Product Category' has high importance in predicting sales success, but stakeholders are concerned about potential bias in this variable. What feature in Einstein Discovery helps understand how this variable impacts predictions?

A company wants to grant external partners access to specific CRM Analytics dashboards without giving them full Salesforce licenses. What licensing and security approach should be recommended?

A recipe needs to transform a dataset where account names are in different formats (e.g., 'ACME Corp.', 'acme corp', 'Acme Corporation'). The consultant wants to standardize these to enable proper grouping. Which recipe transformation is most appropriate?

A dashboard contains a complex SAQL query that performs multiple joins and aggregations, causing slow load times. Users complain about performance. What is the best practice to improve dashboard performance?

A consultant has trained an Einstein Discovery model and wants to evaluate its performance before deployment. The model shows 95% accuracy on the training dataset but only 70% accuracy on the validation dataset. What does this indicate?

An organization has row-level security requirements where managers should see their team's data plus their own, but individual contributors should only see their own data. How should this be implemented in CRM Analytics?

A dataflow needs to combine Account data with aggregated Opportunity data (sum of amounts per account). What is the correct sequence of transformations?

A dashboard designer wants to create a drill-down experience where users start with a regional view, click a region to see countries, then click a country to see cities. What dashboard feature enables this functionality?

An Einstein Discovery model predicts customer lifetime value (CLV) as a continuous numeric value. The business wants to understand which factors most strongly influence higher CLV. What Einstein Discovery feature provides this insight?

A recipe pulls data from a Salesforce object that contains 50 million records, but the resulting dataset only needs 100,000 records based on specific criteria. The recipe is timing out. What optimization should be implemented first?

A business user wants to explore different scenarios in Einstein Discovery, such as 'What if we increase marketing spend by 20%?' What feature allows them to perform this type of analysis?