
Study map for Salesforce CRM Analytics and Einstein Discovery…. Confirm objectives on current Salesforce sources.
Salesforce CRM Analytics and Einstein Discovery Consultant
Catalog status checked July 19, 2026: Available.
Current credential snapshot
| Item | Current listing |
|---|---|
| Credential | Salesforce CRM Analytics and Einstein Discovery Consultant |
| Track | Consultant |
| Level in source inventory | Mid |
| Exam code in source inventory | Analytics-Con-201 |
| Price in source inventory | $200 USD |
| Availability | Available |
Exam format, objectives, pricing, and prerequisites can change. Confirm those details in Salesforce’s current exam guide before purchasing or scheduling an exam.
Study overview
📊 Stop guessing and start predicting with the power of AI-driven insights.Salesforce Certified CRM Analytics and Einstein Discovery Consultant
Mastering the art of data transformation, predictive modeling, and enterprise-grade visualization.
At a Glance
| Feature | Details |
|---|---|
| Exam Code | Analytics-Con-201 |
| Duration | 90 minutes |
| Questions | 60 multiple-choice and 5 unscored |
| Passing Score | 68% |
| Cost | $200 USD |
| Delivery | Proctored online or at a testing center |
| Prerequisites | None |
| Recommended Experience | Minimum 1 year of experience in CRM Analytics domains |
🎯 Who Should Take This – The Data Architect: You understand how to bridge the gap between raw Salesforce objects and optimized columnar datasets for high-performance querying.
- The BI Consultant: You have a knack for translating complex business requirements into actionable dashboards that users actually enjoy using.
- The AI Strategist: You are ready to move beyond descriptive analytics and use Einstein Discovery to provide prescriptive recommendations to stakeholders.
- The Solution Designer: You know how to implement robust security models and deploy complex analytics assets across multi-org environments.
What You’ll Prove
This credential validates your ability to design and implement a full-stack analytics solution within the Salesforce ecosystem. You aren’t just making charts; you are building a data-driven culture by ensuring data integrity, security, and predictive accuracy. – You can design scalable data architectures using Dataflows and Recipes to join disparate data sources.
- You can implement complex security protocols including
Security PredicatesandSharing Inheritance. - You can build advanced visualizations using
SAQLand dynamic bindings for highly interactive user experiences. - You can deploy AI models via Einstein Discovery stories to predict outcomes and suggest improvements directly on records.
- You can manage the lifecycle of analytics assets through auditing, performance tuning, and structured deployment processes.
Exam Outline
| Domain | Weight | What’s Covered |
|---|---|---|
| Data Layer | 24% | Dataflow vs Recipe nodes, data sync, external data ingestion, and dataset management. |
| Analytics Dashboard Design | 19% | Visualization best practices, chart selection, and XMD modifications for appearance. |
| Einstein Discovery Story Design | 19% | Outcome variables, descriptive/predictive insights, and model write-back to Salesforce. |
| Analytics Dashboard Implementation | 18% | SAQL query syntax, bindings, facets, and embedding dashboards in Lightning pages. |
| Security | 11% | Security Predicates, Sharing Inheritance, and Integration vs Security user roles. |
| Administration | 9% | App sharing, Change Sets for deployment, and performance monitoring via Dashboard Inspector. |
💡 In Notion, convert each domain row into a toggle to nest your study notes underneath.
Suggested Study Path
- Review the core architecture by contrasting the JSON-based
Dataflownodes (augment, flatten, digest) with the visual ETL capabilities ofData Prep Recipes. - Master the security model by practicing the implementation of row-level security through
Security Predicatesthat referenceUser Attributefields. - Get hands-on with SAQL to understand how to manually edit dashboard JSON for advanced filtering logic that standard faceting cannot handle.
- Complete the Superbadges specifically the
CRM Analytics SpecialistandEinstein Discovery Story Designbadges to validate your readiness. - Study the Einstein Discovery lifecycle from selecting an outcome variable to interpreting the
GINI coefficientand deploying the model to production. - Simulate a deployment by using
Change Setsor the CLI to move apps and datasets, paying close attention to which elements do not migrate automatically.
⚠️ Watch Out For – Currency conversion logic: Remember that currency values usually display in the Integration User currency unless specific conversion logic is applied.
- Big Object limitations: Be aware that incremental data sync is not supported for Salesforce
Big Objects, which can impact performance for large datasets. - Sharing Inheritance boundaries: Don’t assume it works for every object; know the limits and when you must fallback to a manual
Security Predicate. - Mobile Layout hierarchy: Understand how the system selects a layout when multiple are eligible—it’s often based on the most device properties set.
- SAQL syntax nuances: Watch out for the specific clauses like
foreach,group, andlimitthat appear in the exam’s code-snippet questions.
🔗 Resources – Official Exam Guide
Data is only as good as the decisions it drives. Go build something that makes your users look like geniuses.
Official status sources
Catalog status last verified: 2026-07-19.