EINSTEIN ANALYTICS AND DISCOVERY CONSULTANT

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ABOUT THE EXAM

Candidates should be able to design and implement Salesforce Einstein Analytics and Discovery solutions in a customer-facing role.
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Exam Outline

The Salesforce Einstein Analytics and Discovery Consultant exam measures a candidate’s knowledge and skills related to the following objectives.
Data Layer: 24%

Given data sources, use Data Manager to extract and load the data into the Einstein Analytics application to create datasets. Describe how the Salesforce platform features map to the Model-View-Controller (MVC) pattern.
Given business needs and consolidated data, implement refreshes, data sync (replication), and/or recipes to appropriately solve the basic business need. Identify the common scenarios for extending an application's capabilities using the AppExchange.
Given a situation, demonstrate knowledge of what can be accomplished with the Einstein Analytics API
Given a scenario, use Einstein Analytics to design a solution that accommodates dataflow limits.

Security: 11%

Given governance and Einstein Analytics asset security requirements, implement necessary security settings including users, groups, and profiles.
Given row-based security requirements and security predicates, implement the appropriate dataset security settings.
Implement App sharing based on user, role, and group requirements.

Admin: 9%

Using change management strategies, manage migration from sandbox to production orgs.
Given user requirements or ease of use strategies, manage dataset extended metadata (XMD) by affecting labels, values, and colors.
Given a scenario, improve dashboard performance by restructuring the dataset and/or data using lenses, pages, and filters.
Given business and access requirements, enable Einstein Analytics, options, and access as expected.

Analytics Dashboard Design: 19%

Given a customer situation, determine and define their dashboarding needs.
Given customer requirements, create meaningful and relevant dashboards through the application of user experience (UX) design principles and Einstein Analytics best practices.
Given business requirements, customize existing Einstein Analytics template apps to meet the business needs.

Analytics Dashboard Implementation: 18%

Given business requirements, define lens visualizations such as charts to use and dimensions and measures to display.
Given customer business requirements, develop selection and results bindings with static queries.
Given business expectations, create a regression time series.
Given customer requirements, develop dynamic calculations using compare tables.
Given business requirements that are beyond the standard user interface (UI), use Salesforce Analytics Query Language (SAQL) to build lenses, configure joins, or connect data sources.

Einstein Discovery Story Design: 19%

Given a dataset, use Einstein Discovery to prepare data for story output by accessing data and adjusting outputs.
Given initial customer expectations, analyze the story results and determine suggested improvements that can be presented to the customer.
Given derived results and insights, adjust data parameters, add/remove data, and rerun story as needed.
Describe the process to perform writebacks to Salesforce objects.