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Salesforce Tableau CRM Einstein Discovery Consultant : Tableau-CRM-Einstein-Discovery-Consultant Exam
- Exam Code: Tableau-CRM-Einstein-Discovery-Consultant
- Exam Name: Salesforce Tableau CRM Einstein Discovery Consultant
- Updated: Oct 02, 2026
- Q & A: 244 Questions and Answers
About Salesforce Tableau CRM Einstein Discovery Consultant : Tableau-CRM-Einstein-Discovery-Consultant Exam Questions
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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Discovery | 12% | - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - Analyzing model results and proposing data improvements - Monitoring and interpreting Model Cards to improve or maintain model performance - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Adjusting data parameters and adding/removing data or columns to improve models |
| Topic 2: Analytics Dashboard Implementation | 19% | - Using advanced functionality such as windowing and time series analysis in compare tables - Developing selection/result interactions with different query types - Making dashboards actionable and accessible in Lightning pages - Monitoring and optimizing query performance using Dashboard Inspector - Configuring dashboards using accurate query types and widget-level parameters - Implementing delivery management strategies using versioning and/or Dashboard Publisher |
| Topic 3: Security | 16% | - Implementing security settings for users, groups, and profiles - App sharing based on user and group requirements - Dataset security settings using sharing inheritance and security predicates |
| Topic 4: Administration | 16% | - Managing CRM Analytics environment and configurations - Understanding recipes (Data Prep) for visual ETL interface - Embedding analytics in Salesforce - SAQL query language for dashboard JSON and lens queries |
| Topic 5: Data Layer | 24% | - Incremental sync configuration - Data extraction from sources - Troubleshooting failed dataflow jobs - Understanding denormalized sequential datasets - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets |
| Topic 6: Analytics Dashboard Design | 13% | - Scoping, validating, and prioritizing dashboard design requirements - Creating dashboards following CRM Analytics best practices and UX design principles - Identifying appropriate use and configuration of standard CRM Analytics templated apps |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
Question #1
A company wants to allow users who belong to an account team to see all the Opportunities associated with that Account in Einstein Analytics.
Which two actions accomplish this requirement? Choose 2 answers
- A. Apply sharing inheritance.
- B. In the dataflow, extract the OpportunityTeamMember object and augment it with the Opportunity object using 'Opportunityld' as the join field and apply the following security predicate: 'OpportunityTeamMember.Userld' == "$User.Id".
- C. Create a master-detail relationship between the Salesforce Account and Opportunity objects.
- D. In the dataflow, extract the AccountTeamMember object and augment it with the Opportunity object using 'Accountld' as the join field and apply following security predicate: 'AccountTeamMember.Userld' == "$User.Id".
Correct Answer: A,D 🗳️
Question #2
The Universal Containers company used Einstein Analytics to create two datasets:
Dataset A: contains a list of activities with an "activitylD" dimension and a "userlD" dimension Dataset B: contains a list of users with a "userlD" dimension The team wants to delete from Dataset A all activities related to users in Dataset B.
How can an Einstein Consultant help them achieve this?
- A. Use an external ETL tool to extract both datasets and delete records.
- B. Use a combination of dataflow transformations: "augment" and "filter."
- C. Use the dataflow transformation "delete" and set "userlD" as the deletion ID.
Correct Answer: B 🗳️
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Question #3
How many concurrent stories per org are allowed?
- A. 2
- B. 4
- C. 5
- D. 1
Correct Answer: A 🗳️
Question #4
The client is trying to create a SAQL step to predict sales in each sales region. They cannot get the query to return any results, but have identified that the error is in the time series statement. They have asked an Einstein Consultant to review the following query and fix any errors.
Which timeseries statement will fix the query'
- A. q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region', seasonality=4);
- B. q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (Iength = 12, dateCols=('Date_Year', 'Date_Month". "Y-M"), partition='Region');
- C. q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region');
- D. q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (dateCols=('Date_Year, Date_Quarter', "Y-Q"), partition=Region', ignoreLast=true);
Correct Answer: B 🗳️
Question #5
What are the two main parts of a lens/exploration?
- A. Measure
- B. Grouping
- C. Dataset
- D. Query
- E. Visualization
Correct Answer: B,E 🗳️
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