SALESFORCE CERTIFICATION
Certified CRM Analytics and Einstein Discovery Consultant Practice Exam
Exam Number: 3719 | Last updated 14-Apr-26 | 3220+ questions across 6 vendor-aligned objectives
The Certified CRM Analytics and Einstein Discovery Consultant exam validates your ability to design and implement analytics solutions using Salesforce CRM Analytics (formerly Tableau CRM / Einstein Analytics). It covers dataset creation, dashboard design, SAQL queries, and Einstein Discovery’s predictive modeling capabilities within the Salesforce ecosystem.
Expect about 25% of exam content to cover crm analytics data layer, covering datasets, dataflows, recipes, and external data connectors. Dashboard Design commands 25% of the blueprint, covering charts, tables, bindings, interactions, and SAQL queries. Nearly one-fifth of questions test einstein discovery, covering story creation, model training, predictions, and deployment. These high-weight domains should anchor your study plan and receive the deepest attention.
Beyond the core areas, the exam also evaluates complementary skills. Roughly 15% of the questions address Security and Governance, which spans row-level security, sharing inheritance, and app permissions. Administration and Deployment carries the heaviest weight at 15%, which spans templates, apps, scheduling, and performance optimization. Although individually lighter, these topics frequently appear in scenario-based questions that blend multiple skill areas.
Every answer links to the source. Each explanation below includes a hyperlink to the exact Salesforce documentation page the question was derived from. PowerKram is the only practice platform with source-verified explanations. Learn about our methodology →
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Question #1 - Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
A sales operations director wants to build a CRM Analytics dashboard that shows pipeline health, win rates, and forecast accuracy. The data must refresh daily from Sales Cloud.
What should the consultant configure as the data foundation?
A) A CSV file upload that the sales operations team updates daily
B) A Salesforce report embedded in the CRM Analytics dashboard
C) A dataflow or recipe that extracts Opportunity, Account, and User data from Sales Cloud into CRM Analytics datasets with a daily scheduled refresh
D) Direct live queries to Salesforce objects from the dashboard
Show solution
Correct answers: C – Explanation:
Dataflows and recipes extract, transform, and load Salesforce data into CRM Analytics datasets optimized for dashboard queries. Scheduled daily refreshes ensure data currency. Direct queries to Salesforce objects would be slow for complex analytics. CSV uploads require manual effort and are error-prone. Embedded reports lack the analytical depth and interactivity of CRM Analytics dashboards. Source: Trailhead: CRM Analytics Data Integration
Question #2 - Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
A CRM Analytics consultant is building a dashboard with a bar chart showing revenue by region. The client wants to click on a region bar and have all other dashboard components filter to show data for only that region.
How should the consultant implement this interactivity?
A) Add a dropdown filter at the top of the dashboard for region selection only
B) Create separate dashboards for each region
C) Use SAQL to hardcode each region into separate chart components
D) Configure dashboard faceting or bindings so that selecting a region in the bar chart filters the datasets used by all other dashboard components
Show solution
Correct answers: D – Explanation:
Dashboard faceting and bindings enable interactive filtering where a selection in one component automatically filters data in all linked components. Clicking a region bar filters the entire dashboard to that region’s data. Separate dashboards multiply maintenance. Dropdown filters work but miss the visual interactivity of clicking charts. Hardcoded SAQL creates rigid, unmaintainable components. Source: Trailhead: CRM Analytics Dashboards
Question #3 - Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
A consultant needs to create a CRM Analytics dashboard that combines Salesforce Opportunity data with external financial data stored in a Snowflake database.
How should the consultant bring the external data into CRM Analytics?
A) Configure a CRM Analytics external connector to Snowflake, and use a recipe to join the external data with Salesforce Opportunity datasets
B) Build a custom Apex integration between Salesforce and Snowflake
C) Manually export data from Snowflake and upload CSV files to CRM Analytics
D) Migrate all Snowflake data into Salesforce custom objects first
Show solution
Correct answers: A – Explanation:
CRM Analytics external connectors support direct connections to external databases like Snowflake. Recipes can then join external data with Salesforce datasets, creating unified analytics. Manual CSV uploads are labor-intensive and stale. Migrating all data to custom objects is impractical and wastes storage. Custom Apex does not feed CRM Analytics datasets natively. Source: Trailhead: CRM Analytics Data Integration
Question #4 - Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
A consultant is creating an Einstein Discovery story to predict which sales opportunities are most likely to close. The dataset includes opportunity amount, stage, industry, days in current stage, and number of activities.
What should the consultant do to ensure a high-quality predictive model?
A) Use only the Opportunity Amount field as the sole predictor
B) Include every available field in the dataset regardless of relevance
C) Skip model validation and deploy immediately to production
D) Review the data for quality issues, select relevant features, evaluate the story’s model metrics (R-squared or AUC), and refine by removing fields that add noise or bias
Show solution
Correct answers: D – Explanation:
Quality predictive models require clean data, relevant feature selection, and iterative refinement based on model metrics. R-squared (regression) or AUC (classification) indicate model accuracy. Irrelevant fields add noise. A single predictor limits model power. Deploying without validation risks inaccurate predictions that damage user trust. Source: Trailhead: Einstein Discovery
Question #5 - Scope and roadmap story creation, model training, and predictions to align platform investments with measurable business outcomes and stakeholder priorities
A company wants to embed Einstein Discovery predictions directly into the Salesforce record page so that sales reps see the predicted probability of winning an opportunity and the top factors influencing the prediction.
How should the consultant deploy this?
A) Create a dashboard that reps must navigate to separately from the record page
B) Build a custom Visualforce page that calls the Einstein Discovery API
C) Deploy the Einstein Discovery model and add the Einstein Predictions Lightning component to the Opportunity record page, showing prediction score and key influencers
D) Send prediction reports to sales reps via email weekly
Show solution
Correct answers: C – Explanation:
Einstein Predictions Lightning components embed predictions directly on record pages, showing the score and top influencing factors in the context where reps make decisions. This drives adoption by making insights actionable without leaving the record. Email reports are delayed and disconnected. Separate dashboards require navigation away from the workflow. Custom Visualforce adds development effort for a built-in component. Source: Trailhead: Einstein Prediction Builder
Question #6 - Lock down and govern row-level security, sharing inheritance, and app permissions to safeguard sensitive data and enforce least-privilege access across the organization
A CRM Analytics consultant needs to implement row-level security so that sales reps see only their own opportunity data in dashboards, while managers see data for their entire team.
Which security approach should the consultant configure?
A) Configure security predicates on the dataset that filter rows based on the logged-in user’s role or ownership, inheriting the Salesforce sharing model
B) Set all dashboards to public access and rely on trust
C) Create separate dashboards for each sales rep and manager
D) Remove sensitive data fields from the dataset
Show solution
Correct answers: A – Explanation:
Security predicates on CRM Analytics datasets enforce row-level security by filtering data based on the logged-in user’s identity, role, or ownership — mirroring the Salesforce sharing model. This ensures each user sees only authorized data across all dashboards. Separate dashboards multiply maintenance exponentially. Removing fields addresses column-level security, not row-level. Public access violates data security requirements. Source: Trailhead: CRM Analytics Security
Question #7 - Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
A consultant has built a CRM Analytics recipe that processes millions of opportunity records. The recipe takes 4 hours to run, exceeding the client’s 2-hour refresh window.
What optimization should the consultant implement?
A) Schedule the recipe to run less frequently to accommodate the longer processing time
B) Split the recipe into 10 smaller recipes that each process a subset of data
C) Optimize the recipe by filtering data early in the pipeline, reducing unnecessary joins and transformations, and partitioning the data for incremental processing
D) Increase the CRM Analytics storage allocation
Show solution
Correct answers: C – Explanation:
Recipe optimization follows the principle of reducing data volume early — applying filters before joins and transformations, eliminating unnecessary steps, and using incremental processing to handle only changed records. These techniques can dramatically reduce processing time. Running less frequently sacrifices data freshness. Splitting into many recipes adds orchestration complexity. Storage allocation does not affect processing speed. Source: Trailhead: CRM Analytics Data Integration
Question #8 - Lock down and govern row-level security, sharing inheritance, and app permissions to safeguard sensitive data and enforce least-privilege access across the organization
An executive wants a CRM Analytics dashboard that shows how actual quarterly revenue compares to forecast, with the ability to drill down from company-level to region to individual rep performance.
How should the consultant design this dashboard?
A) Build a single dashboard with hierarchical drill-down using step interactions and bindings that progressively filter from company to region to rep level
B) Show all data on a single page with no filtering capability
C) Create a static PowerPoint presentation with the quarterly data
D) Create three separate dashboards — one for company, one for region, one for rep
Show solution
Correct answers: A – Explanation:
Hierarchical drill-down using CRM Analytics step interactions and bindings provides a seamless experience where executives start at the company level and click to drill into regions, then into individual reps — all within a single dashboard. Separate dashboards break the analytical flow. A single page without filtering overwhelms users. Static presentations lack interactivity and become stale immediately. Source: Trailhead: CRM Analytics Dashboards
Question #9 - Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
A consultant is training an Einstein Discovery model to predict customer churn. The model shows high accuracy (95%) but performs poorly on actual new data after deployment.
What is the most likely cause of this discrepancy?
A) The production data uses a different timezone than the training data
B) The model is overfitting to the training data due to data leakage, too many features, or insufficient training data diversity
C) The Salesforce API is not returning data fast enough for real-time predictions
D) Einstein Discovery has a known bug with churn prediction models
Show solution
Correct answers: B – Explanation:
High training accuracy with poor real-world performance is a classic sign of overfitting. Data leakage (including fields that would not be available at prediction time), too many features relative to training data size, or training data that does not represent the diversity of production data all cause this. API speed affects response time, not accuracy. There is no known bug. Timezone differences would not cause 95% to poor performance gap. Source: Trailhead: Einstein Discovery
Question #10 - Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
A consultant needs to schedule multiple CRM Analytics dataflows and recipes to run in a specific sequence — the Account dataflow must complete before the Opportunity dataflow starts, and both must finish before a consolidated analytics recipe runs.
How should the consultant configure this dependency chain?
A) Manually trigger each dataflow after checking that the previous one completed
B) Use CRM Analytics scheduling with dataflow dependencies or a recipe that chains processing steps, ensuring sequential execution with dependency management
C) Schedule all three to run at the same time and hope they finish in order
D) Create a single massive dataflow that combines all three processes
Show solution
Correct answers: B – Explanation:
CRM Analytics supports dataflow scheduling with dependencies, allowing configuration of execution sequences where downstream flows wait for upstream completion. Recipes can also chain multiple data processing steps. Simultaneous execution with no dependencies risks stale data in dependent datasets. Manual triggering requires human monitoring. A single combined dataflow becomes unwieldy and harder to troubleshoot and maintain. Source: Trailhead: CRM Analytics Data Integration
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Exam mode & learn mode · Score by objective · Updated 14-Apr-26
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What the Certified Crm Einstein Consultant exam measures
- Model and optimize datasets, dataflows, and recipes to ensure clean, scalable data structures that power accurate reporting and integrations
- Craft and refine charts, tables, and bindings to deliver intuitive, responsive interfaces that drive user adoption and productivity
- Scope and roadmap story creation, model training, and predictions to align platform investments with measurable business outcomes and stakeholder priorities
- Lock down and govern row-level security, sharing inheritance, and app permissions to safeguard sensitive data and enforce least-privilege access across the organization
- Promote and govern templates, apps, and scheduling to ship changes safely through structured release pipelines with rollback capabilities
How to prepare for this exam
- Review the official exam guide
- Complete the CRM Analytics trail on Trailhead — work through dataset creation, dashboard building, and Einstein Discovery modules
- Build an analytics dashboard in a Developer Org that pulls from multiple Salesforce objects, includes SAQL customizations, and uses Einstein Discovery predictions
- Take on an analytics project at your organization — even creating executive dashboards from existing data provides valuable hands-on experience
- Focus on Data Layer and Dashboard Design first — they combine for 50% of the exam weight
- Use PowerKram’s learn mode for analytics-specific practice
- Validate readiness in PowerKram’s exam mode
Career paths and salary outlook
Analytics consultants with Salesforce expertise serve organizations seeking data-driven decision-making:
- CRM Analytics Consultant — $115,000–$160,000 per year, building dashboards and predictive models (Glassdoor salary data)
- Salesforce Data Analyst — $90,000–$130,000 per year, translating CRM data into business insights (Indeed salary data)
- Einstein Analytics Architect — $140,000–$185,000 per year, designing enterprise analytics strategy on Salesforce (Glassdoor salary data)
Official resources
Follow the CRM Analytics and Einstein Discovery Learning Path on Trailhead. The official exam guide provides the full objective breakdown.