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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analytics Dashboard Design | 13% | - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Scoping, validating, and prioritizing dashboard design requirements - Creating dashboards following CRM Analytics best practices and UX design principles |
| Topic 2: 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 |
| Topic 3: Einstein Discovery | 12% | - Monitoring and interpreting Model Cards to improve or maintain model performance - Adjusting data parameters and adding/removing data or columns to improve models - 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) - Analyzing model results and proposing data improvements |
| Topic 4: Data Layer | 24% | - Dataflow configuration for multi-object scenarios - Incremental sync configuration - Data extraction from sources - Troubleshooting failed dataflow jobs - Data transformation and loading into Einstein datasets - Understanding denormalized sequential datasets |
| Topic 5: Analytics Dashboard Implementation | 19% | - 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 - Using advanced functionality such as windowing and time series analysis in compare tables - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Developing selection/result interactions with different query types |
| Topic 6: Administration | 16% | - Embedding analytics in Salesforce - SAQL query language for dashboard JSON and lens queries - Understanding recipes (Data Prep) for visual ETL interface - Managing CRM Analytics environment and configurations |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
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) Create a master-detail relationship between the Salesforce Account and Opportunity objects.
B) 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' == "$User.Id".
C) Apply sharing inheritance.
D) In the dataflow, extract the OpportunityTeamMember object and augment it with the Opportunity object using 'Opportunityld' as the join field and apply the following security predicate: 'OpportunityTeamMember.Userld' == "$User.Id".
2. Universal Containers (UC) is a multinational company that utilizes Salesforce and has a variety of internal systems. UC uses Einstein Analytics for their data analysis platform and they want to automate their weekly manual process to create a dataset from their on-premise data warehouse. Which solution should a consultant recommend to meet this requirement?
A) Utilize middleware with Analytics External Data API
B) Utilize a dataflow
C) Utilize a Salesforce weekly export feature
D) Utilize Analytics Connector.
3. Universal Containers has a Sales Manager dashboard- They have a compare table that displays the count of Customers and the Sum of Annual Revenue per year and per Account type. A consultant is asked to add more groupings to enhance details about their customers.
What is the maximum number of grouping columns that the consultant must be aware of in order to continue using a compare table versus creating the query using SAQL?
A) unlimited
B) 4
C) 8
D) 10
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) Click Create Story and Einstein will take care of the rest.
B) Select the isWon = True outcome variable and generate the story.
C) Filter to only look at isWon = True records to avoid skewing the results.
D) Change the grain by removing the line level detail as it will skew the win rate.
5. Can you share data from a widget?
A) Only with the proper Einstein Analytics Platform license.
B) Select the step of the widget and share data.
C) No, you can't share data from a widget.
D) Select "Share" icon, click on "Download", and select "Download in .png, .XLS, or CSV" format.
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: D |
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