"Salesforce Tableau CRM Einstein Discovery Consultant", also known as Tableau-CRM-Einstein-Discovery-Consultant exam, is a Salesforce Certification. With the complete collection of questions and answers, BraindumpsPass has assembled to take you through 244 Q&As to your Tableau-CRM-Einstein-Discovery-Consultant Exam preparation. In the Tableau-CRM-Einstein-Discovery-Consultant exam resources, you will cover every field and category in Salesforce Consultant Certification helping to ready you for your successful Salesforce Certification.
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| Section | Weight | Objectives |
|---|---|---|
| Data Layer | 24% | - Data transformation and loading into Einstein datasets - Dataflow configuration for multi-object scenarios - Incremental sync configuration - Understanding denormalized sequential datasets - Data extraction from sources - Troubleshooting failed dataflow jobs |
| 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 |
| Administration | 16% | - SAQL query language for dashboard JSON and lens queries - Managing CRM Analytics environment and configurations - Embedding analytics in Salesforce - Understanding recipes (Data Prep) for visual ETL interface |
| Einstein Discovery | 12% | - Analyzing model results and proposing data improvements - Adjusting data parameters and adding/removing data or columns to improve models - Monitoring and interpreting Model Cards to improve or maintain model performance - Building models by assessing data and selecting prediction types (numeric, binary, multi-classification) - Enabling prediction features on Lightning record pages across Salesforce and CRM Analytics |
| Analytics Dashboard Implementation | 19% | - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Monitoring and optimizing query performance using Dashboard Inspector - 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 - Configuring dashboards using accurate query types and widget-level parameters |
| Security | 16% | - Dataset security settings using sharing inheritance and security predicates - Implementing security settings for users, groups, and profiles - App sharing based on user and group requirements |
Why would you use a merge key in a dataset recipe?
Correct Answer: C 🗳️
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
Correct Answer: C,D 🗳️
A large company has a single dataset that contains the attainment and commission fields for all sales reps. Each sales rep should be able to view the attainment data for each rep in their division. Each rep should only be able to see their own commission data.
Which option should be used to enforce this requirement?
Correct Answer: B 🗳️
Refer to the graphic.
Which conclusion can be made regarding the strength of the model shown?
Correct Answer: C 🗳️
Exhibit:
A shipping company created a dataset. "Bookll," containing budget per region per month for the first six months of 2018. The dataset is shown in the graphic. Now, they want to create a lens showing the total budget for each region for each month. Every combination of region and month must be shown in the lens, even if there is no data.
How can an Einstein Consultant help this company build this lens?
Correct Answer: A 🗳️
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