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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Administration | 16% | - Understanding recipes (Data Prep) for visual ETL interface - SAQL query language for dashboard JSON and lens queries - Embedding analytics in Salesforce - Managing CRM Analytics environment and configurations |
| Topic 2: Security | 16% | - App sharing based on user and group requirements - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles |
| Topic 3: Data Layer | 24% | - Troubleshooting failed dataflow jobs - Data extraction from sources - Incremental sync configuration - Understanding denormalized sequential datasets - Data transformation and loading into Einstein datasets - Dataflow configuration for multi-object scenarios |
| Topic 4: Analytics Dashboard Design | 13% | - Creating dashboards following CRM Analytics best practices and UX design principles - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Scoping, validating, and prioritizing dashboard design requirements |
| Topic 5: Analytics Dashboard Implementation | 19% | - Monitoring and optimizing query performance using Dashboard Inspector - Configuring dashboards using accurate query types and widget-level parameters - Developing selection/result interactions with different query types - Using advanced functionality such as windowing and time series analysis in compare tables - Making dashboards actionable and accessible in Lightning pages - Implementing delivery management strategies using versioning and/or Dashboard Publisher |
| Topic 6: Einstein Discovery | 12% | - Monitoring and interpreting Model Cards to improve or maintain model performance - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics - 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 - Analyzing model results and proposing data improvements |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
Question 1
An Einstein Discovery team created a model to maximize the margin of their sales opportunities. They want to deploy the model to the Opportunity object in order to predict the outcome of every newly created or updated Opportunity.
What are the steps to accomplish this?
A. Create a trigger on Opportunity and use the REST API to get predictions from Einstein Discovery.
B. Create a trigger on Opportunity and use the Salesforce External Connector to get predictions from Einstein Discovery.
C. Create an Apex batch on Opportunity and use the REST API to get predictions from Einstein Discovery.
D. Create a trigger on Opportunity and install the Einstein Discovery Writeback managed package from the AppExchange.
Question 2
A new field called "CardBalance" is added to the standard object. Account. The team wants to add this new field to an Einstein Analytics dataset that sources the Account object; however, the team needs to limit the users who are able to view that field's data in the dataset.
How can an Einstein Consultant help them implement this new requirement?
A. Add the new field to the existing dataset and use the XMO file parameters to hide the new field from users who should not have access to it.
B. Create a new Account dataset containing the new field and give access to this dataset only to users who should be able to see the new field.
C. Add the new field to the existing dataset and use "Security Predicates' to hide it from users who should not access it.
D. Add the new field to the existing dataset and activate "Sharing inheritance" in the Account object so only users who have access to the field in Salesforce will have access in Einstein Analytics.
Question 3
The Universal Containers company thinks it knows everything about its business. However, Einstein Discovery surfaces an unexpected pattern that is concerning. They call in departmental experts and hold a meeting to discuss next steps with an Einstein Consultant.
What should the consultant advise as the next action'
A. Accept the new pattern and have confidence that Einstein knows the business as accurately as the customer.
B. Determine if the pattern is a data issue or a new insight.
C. Consult a Data Scientist for further analysis.
D. Filter out the data that causes the unexpected pattern and analyze the new results.
Question 4
A client has a dataset comprised of Opportunity data and Opportunity Line level dat a. The client wants to use this dataset with Einstein Discovery to understand the Opportunity win rate.
What must be done to the dataset to achieve this goal?
A. Filter to only look at isWon = True records to avoid skewing the results.
B. Click Create Story and Einstein will take care of the rest.
C. Select the isWon = True outcome variable and generate the story.
D. Change the grain by removing the line level detail as it will skew the win rate.
Question 5
Max number of dataflow definitions (with data sync enabled)
A. 25
B. 15
C. 35
D. 55
Solutions:
| Question 1 Answer: D | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: C |
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