What is Salesforce CRM Analytics?

CRM Analytics (formerly Tableau CRM, formerly Einstein Analytics) is Salesforce's native business intelligence and analytics platform. It allows users to connect Salesforce and external data, build interactive dashboards, and surface AI-powered insights within the Salesforce UI. The CRM Analytics and Einstein Discovery Consultant certification validates skills in designing, building, and securing analytics solutions. The exam has 60 questions, 105-minute time limit, ~67% passing score, and a $200 fee.

What is the difference between a dataflow and a recipe in CRM Analytics?

Dataflows are the original ETL mechanism in CRM Analytics — they use JSON-based transformation nodes (augment, filter, computeExpression, flatten, sfdcDigest, sfdcRegister) and run on a scheduled job. Recipes are the newer visual ETL builder — a drag-and-drop Data Prep UI with node types for joining, filtering, aggregating, and transforming data. Recipes are easier to build and maintain for most use cases. Dataflows offer more control for complex transformations and are still used in many legacy implementations.

What is SAQL in CRM Analytics?

SAQL (Salesforce Analytics Query Language) is the query language used by CRM Analytics to query datasets. It resembles SQL but is designed for CRM Analytics datasets rather than relational databases. Key functions: q() wraps the dataset reference, groupby groups results, foreach iterates over groups, cogroup joins two datasets. SAQL is used in lens and dashboard queries when the visual designer is insufficient for complex calculations.

What is row-level security in CRM Analytics?

Row-level security (RLS) in CRM Analytics controls which rows of a dataset a user can see when they view a dashboard or lens. It is implemented via security predicates — filter expressions evaluated per user based on their attributes (role, profile, user fields). For example, a predicate like 'Region' == "$User.Region__c" would limit each user to rows matching their assigned region. Without RLS, all users with dataset access see all rows regardless of their Salesforce record-level access.

How long should I study for the CRM Analytics exam?

Plan for 8–10 weeks with 10–12 hours per week. CRM Analytics requires hands-on experience — many exam questions test specific configuration steps in Analytics Studio that you cannot learn from reading alone. Use a free CRM Analytics developer org (available via Trailhead Playground) to build real dataflows, recipes, and dashboards. The Data Integration section (23%) is the most heavily weighted and requires practical dataflow/recipe knowledge.

Study Guide

Salesforce CRM Analytics Study Guide (Winter '26)

Your complete guide to passing the CRM Analytics exam — dataflows, recipes, SAQL, dashboard design, row-level security, and analytics apps.

Written and reviewed by Krishna Mohan — ADM-201, PD1, PD2, App Builder & Consultant certified. Updated for Winter '26. Methodology · Contact

Questions

105 min

Time Limit

~67%

Passing Score

$200

Exam Fee

Exam Sections & Weightings

Data Integration23%

Dashboards & Lenses19%

Analytics Projects & Solutions18%

Analytics Ecosystem & Admin17%

Security & Row-Level Security12%

SAQL & Recipes11%

What Each Section Tests

23%

Data Integration

Dataflows: transformations (augment, computeExpression, flatten, filter, sfdcDigest, sfdcRegister), scheduling, output datasets. Recipes: Data Prep UI, node types (input, output, formula, bucket, join, filter, aggregate), recipe scheduling. External data upload: CSV connector, SFTP. Connected datasets: live Salesforce data without dataflow. When to use dataflows vs recipes.

19%

Dashboards & Lenses

Dashboard designer: widget types (chart, table, number, filter, date), binding types (static, column, measure). SAQL in dashboards: inline queries. Lenses: exploratory analysis on a single dataset. Dashboard JSON editor for advanced customisation. Faceting: how widgets filter each other. Global filters and local filters. Mobile dashboard considerations.

18%

Analytics Projects & Solutions

Analytics templates: pre-built app templates for specific use cases (Sales Analytics, Service Analytics, CRM Analytics for Financial Services). Customising templates. Creating apps from scratch vs from templates. Analytics Studio navigation: apps, datasets, dashboards, lenses, recipes. Embedded analytics in Salesforce pages using Wave dashboard component or Analytics tab.

17%

Analytics Ecosystem & Admin

Platform overview: Analytics Studio, Data Manager, Data Integration Studio. Dataset limits and row counts. Scheduled dataflow and recipe jobs: run order, failure handling, dependency. Analytics notifications: alert users when metric thresholds are crossed. Analytics home page, navigation, and user experience configuration.

12%

Security & Row-Level Security

Dataset-level security: private vs shared datasets. Row-level security (RLS): predicate logic, security predicates on datasets to filter records per user. Sharing inheritance from Salesforce: replicating record-level security in analytics datasets using role hierarchy sharing. User attributes: dynamic predicates based on user profile fields. Dataset sharing with groups and roles.

11%

SAQL & Recipes

SAQL (Salesforce Analytics Query Language): q() function, groupby, foreach, order, limit, offset, cogroup for joins. SAQL in lenses vs in dashboard queries. Recipes formula nodes: date functions, string manipulation, conditional logic, bucket expressions. Common recipe transformations for data cleansing and enrichment.

8-Week Study Plan

Week 1CRM Analytics overview — enable CRM Analytics in a Trailhead Playground. Navigate Analytics Studio. Create your first dataset from Salesforce data using a dataflow.

Week 2Dataflows — build a dataflow with sfdcDigest, augment (join), filter, and computeExpression nodes. Register the output dataset. Schedule the dataflow and review the Monitor tab.

Week 3Recipes — build a recipe in Data Prep. Join two datasets. Add formula and bucket nodes. Schedule the recipe. Compare the recipe approach to the equivalent dataflow.

Week 4Dashboard design — build a dashboard with chart, table, number, and filter widgets. Configure faceting between widgets. Add a date filter and a global filter bar.

Week 5Lenses and SAQL — explore a dataset using the lens builder. Write basic SAQL queries: groupby, foreach, order, limit. Use cogroup to join two datasets in a SAQL query.

Week 6Row-level security — configure a security predicate on a dataset. Test that different users see different rows. Implement sharing inheritance from Salesforce using role hierarchy.

Week 7Analytics templates and apps — explore the Sales Analytics app template. Customise an existing template. Understand the template structure (JSON files, assets, dataflows).

Week 8Full mock exams. Data Integration (23%) and Dashboards (19%) are the largest sections. Focus on dataflow transformation node types and security predicate logic. Aim for 75%+.

Scenario Strategy Tips

1.Dataflows vs recipes: For complex transformations requiring custom JSON logic, use a dataflow. For visual, maintainable ETL without coding, use a recipe. When both could work, the exam typically favours recipes as the modern recommended approach.
2.RLS predicates must be applied to every dataset: Row-level security does not cascade. If a user joins two datasets in a dashboard query, the predicate must be applied to both datasets separately. Forgetting this is a common exam mistake.
3.Faceting vs filtering: Faceting allows widgets to filter other widgets on the same dashboard when a user makes a selection. It is enabled per-widget in the dashboard designer. Global filters apply to all widgets simultaneously. Know which to use for a given requirement.
4.Connected datasets for live data: If the exam describes a need for real-time or very fresh data without a dataflow/recipe delay, connected datasets (live query to Salesforce) is the answer — at the cost of slower query performance than indexed datasets.

Mock Exam Benchmark

Aim for 75%+ on practice exams before scheduling. CRM Analytics is very tool-specific — knowing the exact transformation nodes in a dataflow (sfdcDigest, augment, computeExpression, flatten) and SAQL syntax is tested directly. There is no shortcut other than hands-on practice in the tool.

Top 10 Concepts to Review

Dataflow transformation nodes: sfdcDigest, augment, computeExpression, flatten, filter, sfdcRegister
Recipes vs dataflows: when to use each, how they differ in architecture
SAQL: q(), groupby, foreach, cogroup, order, limit
Dashboard widgets: chart, table, number, filter, date — and configuration options
Binding types: static, column, measure — how they link widgets to queries
Faceting: enabling between widgets, how user selection propagates
Row-level security predicates: syntax, user attributes, testing RLS
Sharing inheritance: replicating Salesforce record access in analytics datasets
Connected datasets: live query to Salesforce, performance trade-offs
Analytics templates: structure, customisation, app creation from templates

Frequently Asked Questions

What is Salesforce CRM Analytics?: CRM Analytics (formerly Tableau CRM, formerly Einstein Analytics) is Salesforce's native business intelligence and analytics platform. It allows users to connect Salesforce and external data, build interactive dashboards, and surface AI-powered insights within the Salesforce UI. The CRM Analytics and Einstein Discovery Consultant certification validates skills in designing, building, and securing analytics solutions. The exam has 60 questions, 105-minute time limit, ~67% passing score, and a $200 fee.
What is the difference between a dataflow and a recipe in CRM Analytics?: Dataflows are the original ETL mechanism in CRM Analytics — they use JSON-based transformation nodes (augment, filter, computeExpression, flatten, sfdcDigest, sfdcRegister) and run on a scheduled job. Recipes are the newer visual ETL builder — a drag-and-drop Data Prep UI with node types for joining, filtering, aggregating, and transforming data. Recipes are easier to build and maintain for most use cases. Dataflows offer more control for complex transformations and are still used in many legacy implementations.
What is SAQL in CRM Analytics?: SAQL (Salesforce Analytics Query Language) is the query language used by CRM Analytics to query datasets. It resembles SQL but is designed for CRM Analytics datasets rather than relational databases. Key functions: q() wraps the dataset reference, groupby groups results, foreach iterates over groups, cogroup joins two datasets. SAQL is used in lens and dashboard queries when the visual designer is insufficient for complex calculations.
What is row-level security in CRM Analytics?: Row-level security (RLS) in CRM Analytics controls which rows of a dataset a user can see when they view a dashboard or lens. It is implemented via security predicates — filter expressions evaluated per user based on their attributes (role, profile, user fields). For example, a predicate like 'Region' == "$User.Region__c" would limit each user to rows matching their assigned region. Without RLS, all users with dataset access see all rows regardless of their Salesforce record-level access.
How long should I study for the CRM Analytics exam?: Plan for 8–10 weeks with 10–12 hours per week. CRM Analytics requires hands-on experience — many exam questions test specific configuration steps in Analytics Studio that you cannot learn from reading alone. Use a free CRM Analytics developer org (available via Trailhead Playground) to build real dataflows, recipes, and dashboards. The Data Integration section (23%) is the most heavily weighted and requires practical dataflow/recipe knowledge.

What Comes After This Certification?

After this certification, consider: Sales Cloud Consultant, Service Cloud Consultant, or Experience Cloud Consultant.

Exam Section Difficulty Heatmap

Which sections are a gimme vs which ones trap confident candidates. Use this to prioritise your final-week revision.

Exam Section	Difficulty	Study Tip
CRM Analytics Setup	Moderate	Dataset creation and data prep — know the difference between recipe and lens.
Data Preparation and Datasets	Hard	SAQL and dataflow — syntax and transformation order are common failure points.
Lens, Dashboards, and Stories	Trap ⚠	Lens vs dashboard vs story — and when to use Explorer — exam tests these distinctions.
Einstein Discovery	Moderate	Story creation and prediction — outcome vs predictor and model interpretation.

Difficulty based on analysis of common candidate errors across each exam section.

Ready to Practice?

Free CRM Analytics practice questions covering dataflows, recipes, SAQL, and dashboard design.

Start Free Practice Questions