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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Configuring dashboards using accurate query types and widget-level parameters - Making dashboards actionable and accessible in Lightning pages - Monitoring and optimizing query performance using Dashboard Inspector |
| Topic 2: 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 |
| 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: Einstein Discovery | 12% | - Analyzing model results and proposing data improvements - 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 |
| Topic 5: Data Layer | 24% | - Troubleshooting failed dataflow jobs - Data extraction from sources - Data transformation and loading into Einstein datasets - Dataflow configuration for multi-object scenarios - Understanding denormalized sequential datasets - Incremental sync configuration |
| Topic 6: Administration | 16% | - Embedding analytics in Salesforce - Managing CRM Analytics environment and configurations - Understanding recipes (Data Prep) for visual ETL interface - SAQL query language for dashboard JSON and lens queries |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
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 an Apex batch on Opportunity and use the REST API to get predictions from Einstein Discovery.
B) Create a trigger on Opportunity and install the Einstein Discovery Writeback managed package from the AppExchange.
C) Create a trigger on Opportunity and use the Salesforce External Connector to get predictions from Einstein Discovery.
D) Create a trigger on Opportunity and use the REST API to get predictions from Einstein Discovery.
2. In the list of insights, the first insights that you see are the ones that explain, statistically, the most variation in the outcome variable. The insights that appear later, as you scroll through the story, explain variables that, statistically, account for less of the variation in the outcome variable.
When you select one variable, what are you comparing?
A) Selected variable average with the mean
B) Selected variable with CLV
C) Selected variable with a different variable
D) Selected variable with all story data
E) Selected variable average with the filtered average
3. A consultant created an Einstein Analytics dashboard in a sandbox. Now, the dashboard needs to be migrated into production.
To complete the migration, what are the consultant's three options? Choose 3 answers
A) Analytics External Data API
B) Change sets
C) Ant Migration Tool
D) Analytics dashboard connector
E) Analytics REST API
4. What is an Analytics app?
A) Analytics for iPad
B) A specialized version of the Einstein Analytics platform that runs outside of Salesforce
C) The blueprint for your org's Einstein Analytics implementation
D) A collection of datasets, dashboards, lenses, and a dataflow
5. Trending data limits: Maximum number of trended datasets per user and rows per snapshot:
A) 10 per user, 200,000 per snapshot
B) 10 per user, 100,000 per snapshot
C) 5 per user, 200,000 per snapshot
D) 5 per user, 100,000 per snapshot
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: B,C,E | Question # 4 Answer: D | Question # 5 Answer: D |
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