CRM Analytics and Einstein Discovery Consultant
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Core Resources
Exam Overview
From the official exam guide
Questions
65
Duration
105 min
To Pass
63%
Pricing
$200 registration · $100 retake
Delivery
Online proctored or at a testing center
Experience
Recommended: experience designing CRM Analytics solutions and building datasets and dashboards
Prerequisites
No prerequisites required
Exam Topics
Each topic section shows the topic weight, learning objectives, and links to study resources.
Admin/Configuration17%
Permission set licenses (CRM Analytics Plus, Einstein Predictions), enabling CRM Analytics features, data sync and dataflow limits, CRM Analytics API capabilities, and deployment across environments using change sets and Metadata API.
- Given business and access requirements, enable CRM Analytics along with its features, encompassing permission sets and licenses.
- Given a scenario, use CRM Analytics to design a solution that accommodates data sync/dataflows/recipes limits.
- Given a situation, demonstrate knowledge of what can be accomplished with the CRM Analytics API.
- Given business requirements, migrate between different environments for deployment.
Resources
Data Layer23%
Data Manager and data syncs for Salesforce object ingestion, dataflows vs. recipes (node-based ETL), data transformations, refresh scheduling, sync and dataflow limits, dataset extended metadata (XMD), and deployment versioning.
- Given data sources, use Data Manager to extract and load the data into the CRM Analytics application to create datasets.
- Given business needs and consolidated data, implement refreshes for data syncs and dataflows/recipes while keeping limits and considerations in mind.
- Given business/user requirements, perform data transformations in dataflows/recipes.
- Given user requirements or ease of use strategies, manage dataset extended metadata (XMD) by editing labels, values, and colors.
- Implement delivery management strategies in dataflows/recipes including versioning and conversion.
Resources
Security16%
CRM Analytics asset security settings, permission sets and profiles for platform access, row-level dataset security using sharing inheritance vs. security predicates, app sharing roles (viewer/editor/manager), and governance controls.
- Given governance and CRM Analytics asset security requirements, implement necessary security settings for users, groups, and profiles.
- Given row-based security requirements, implement the appropriate dataset security settings by using sharing inheritance and security predicates.
- Implement app sharing based on user and group requirements.
Resources
Analytics Dashboard Design13%
Scoping and prioritizing dashboard requirements, CRM Analytics UX design best practices, templated app selection (Sales Analytics, Service Analytics), mobile layouts, and app structure and navigation design.
- Given business requirements, scope, validate, and prioritize dashboard design requirements.
- Create appropriate dashboards to meet business requirements following CRM Analytics best practices and UX design principles.
- Identify the appropriate use and configuration of a standard CRM Analytics templated app to meet business requirements.
Resources
Analytics Dashboard Implementation19%
SAQL query types (aggregateflex, compare, timeseries, values), widget parameter bindings, step interactions (selection, static, result steps), compare table windowing and time series analysis, embedding dashboards in Lightning pages, Dashboard Inspector for performance optimization, and Dashboard Publisher versioning.
- Given business requirements, configure dashboards using accurate query types and widget level parameters.
- Given business requirements, develop selection/result interactions with different types of queries.
- Given business requirements, use advanced functionality such as windowing and time series analysis within compare tables.
- Given business requirements, make dashboards actionable and accessible in Lightning pages.
- Given a scenario, monitor and optimize query performance using Dashboard Inspector.
- Implement delivery management strategies using versioning and/or Dashboard Publisher.
Resources
Einstein Discovery12%
Einstein Discovery model types (numeric regression, binary classification, multi-classification), data preparation for model building, story vs. model distinction, Model Card interpretation (AUC, RMSE, confusion matrix), deploying predictions on Lightning record pages, and the improvement loop for model performance.
- Build a model by assessing data and selecting one of the three types of predictions (numeric, binary, multi-classification).
- Given business requirements, analyze the model results and propose data improvements to the customer.
- Given derived results and insights from the model, adjust data parameters and add/remove data or columns to improve the model.
- Enable prediction features on Lightning record pages across Salesforce and CRM Analytics.
- Monitor and interpret a Model Card to improve or maintain model performance.
Resources
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