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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Security | 16% | - Dataset security settings using sharing inheritance and security predicates - App sharing based on user and group requirements - Implementing security settings for users, groups, and profiles |
| Topic 3: Data Layer | 24% | - Dataflow configuration for multi-object scenarios - Data transformation and loading into Einstein datasets - Understanding denormalized sequential datasets - Troubleshooting failed dataflow jobs - Data extraction from sources - Incremental sync configuration |
| Topic 4: Analytics Dashboard Design | 13% | - Scoping, validating, and prioritizing dashboard design requirements - Identifying appropriate use and configuration of standard CRM Analytics templated apps - Creating dashboards following CRM Analytics best practices and UX design principles |
| Topic 5: Analytics Dashboard Implementation | 19% | - Using advanced functionality such as windowing and time series analysis in compare tables - Making dashboards actionable and accessible in Lightning pages - Developing selection/result interactions with different query types - Monitoring and optimizing query performance using Dashboard Inspector - Implementing delivery management strategies using versioning and/or Dashboard Publisher - Configuring dashboards using accurate query types and widget-level parameters |
| 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) - Analyzing model results and proposing data improvements - Adjusting data parameters and adding/removing data or columns to improve models |
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
1. Max of objects that can be enabled for Data Sync
A) 200
B) 100
C) 50
D) 150
2. What permissions are necessary for someone to edit the XMD?
A) Manage Analytics Templated Apps
B) Upload External Data to Analytics
C) Edit Dataset Recipes
D) Edit Analytics Dataflows
3. Results from an Einstein Discovary story are reviewed with a business user. They agree with the findings but noticed that none of the fields used in the story have a correlation value greater than 4%. The client is now concerned that the model may not be good enough to next steps?
A) Proceed with deployment if the model quality metric values ire sufficient.
B) Rerun and update the story with a different algorithm.
C) Identify additional data that may have a stronger relationship with the outcome variable.
D) Edit the model accuracy settings and rerun the story.
4. Why would you use a merge key in a dataset recipe?
A) To find out what the merge icons represent in a dataset
B) To join related data from different sources
C) To schedule when a dataset recipe runs
D) To deal with duplicate records
5. A Tableau CRM consultant develops a dashboard called
SalesPerformance_l in a sardbox environment. To deploy the recipe the consultant creates a package in workbench What is the correct meta data reference in the xml file?
A) SalesPefformance_1
B) SalesPerformance_1 EADashboard
C) SalesPerformance_1 waveDashboard
D) SalesPerformance_1 EdgeOashboard
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B,D | Question # 3 Answer: A,C | Question # 4 Answer: B | Question # 5 Answer: C |
