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Cert++

Resource Pack

CRM Analytics and Einstein Discovery Consultant

The Salesforce Certified CRM Analytics and Einstein Discovery Consultant credential is for anyone designing and building analytics solutions on the Salesforce platform. It tests your ability to architect data pipelines, configure row-level security, build dashboards with SAQL queries, and deploy Einstein Discovery predictive models end-to-end. This is a consultant-level exam, so it asks you to make trade-off decisions, not just recall features. If you have been building CRM Analytics apps in the real world, this cert formalizes what you already know. If you are new to the platform, plan on spending real time in a Developer Edition org before you sit the exam.

3-Step Path to Passing

  1. 1

    Complete the CRM Analytics Trailmix

    Start with the official Salesforce trailmix. It covers all six exam domains and includes the cert prep modules with scenario questions and flashcards. Work through the Build and Administer CRM Analytics trail alongside it for hands-on practice.
  2. 2

    Attempt Practice Exams

    I recommend my own practice exams, but I have linked other options in the Study Resources section below. The Data Layer and Security topics trip up a lot of candidates, so pay close attention to those scenario questions.
  3. 3

    Schedule Your Exam

    Exams run every day, at all hours of the day. Schedule with short notice when you feel ready.

Core Resources

Exam Overview

Questions

65

60 scored + 5 unscored

Duration

105 min

1 hour 45 minutes

To Pass

63%

Minimum Score

Question Format

The exam tests solution design judgment across CRM Analytics administration, data pipeline architecture, dataset security, dashboard implementation, and Einstein Discovery model building and deployment.

Scored

92%

60questions

Unscored

8%

5questions

Exam Details

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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