Exam Code: Analytics-Con-201
Exam Name: Salesforce Certified CRM Analytics and Einstein Discovery Consultant
Price: $68.00 $58.88
Exam Questions: 116 Q&As
Last Updated: 2026-10-05
Prepare for the Salesforce Analytics-Con-201 exam with focused coverage of CRM Analytics administration, data preparation, dataset security, dashboard design, dashboard implementation, and Einstein Discovery.
The Salesforce Certified CRM Analytics and Einstein Discovery Consultant certification validates the ability to design, build, deploy, secure, and support enterprise analytics solutions that turn business data into useful insights and predictions.
This consultant-level exam measures practical knowledge of CRM Analytics and Einstein Discovery across the full solution lifecycle. Candidates should be able to translate business requirements into datasets, secure access, build usable dashboards, optimize performance, deploy assets between environments, and evaluate predictive models.
A strong candidate has at least one year of hands-on experience across CRM Analytics and Einstein Discovery. Useful experience includes data ingestion, recipes and dataflows, SAQL, dashboard JSON, security predicates, app permissions, model evaluation, and solution deployment.
The exam contains 60 scored multiple-choice or multiple-select questions and may include up to five unscored questions mixed into the appointment. Because the unscored items are not identified, treat every question as scored and budget about 90 seconds per item.
Enable CRM Analytics features, manage licenses and permission sets, plan around platform limits, understand supported API use cases, and migrate assets between environments.
Use Data Manager and appropriate ingestion methods to create datasets. Know when templated apps, data sync, recipes, and dataflows provide the most maintainable solution.
Implement dataset and app access with security predicates, sharing inheritance, permissions, and user provisioning. Expect scenarios in which technically valid access is still too broad.
Choose visualizations, layouts, and interaction patterns that match the business question and user experience. Mobile use and meaningful KPI presentation matter here.
Build queries, bindings, compare-table calculations, actionable dashboards, embedded experiences, and delivery workflows. Use Dashboard Inspector when the real issue is query performance.
Select an appropriate prediction type, interpret model results and Model Cards, improve input data, and surface predictions or recommendations in Salesforce experiences.
Separate app access from row-level dataset security, data sync from downstream recipe or dataflow processing, and dashboard design decisions from dashboard implementation mechanics. The best answer usually addresses the layer named in the requirement without adding unnecessary customization.
Days 1–4: ingestion, datasets, recipes, dataflows, and limits. Days 5–7: permissions, sharing inheritance, and security predicates. Days 8–10: dashboard queries, bindings, JSON, SAQL, and performance. Days 11–12: Einstein Discovery. Use the final two days for deployment scenarios and timed mixed sets.
Data Layer and Dashboard Implementation together account for 42% of the exam. Memorizing feature names is not enough: practice tracing data from source to dataset, explaining when security is applied, and diagnosing why a dashboard or model does not behave as expected.
The supplied demo contains 10 study questions. The five representative questions below cover administration, security, dashboard delivery, data management, and Einstein Discovery. They are practice material, not official Salesforce exam questions.
During the deployment of CRM Analytics, what is the best method to migrate configurations between environments?
Correct answer: D
Explanation: The Metadata API supports controlled migration of CRM Analytics metadata between environments and is better suited to repeatable deployment than rebuilding configurations manually.
Common mistake: Choosing Change Sets because they are familiar for Salesforce configuration deployments without checking whether the required CRM Analytics assets are supported.
Why the other options are wrong: A is slow and prone to configuration drift. B moves data rather than the required configuration metadata. C does not provide the same coverage for this migration scenario.
Which considerations should be made when configuring security predicates in CRM Analytics? (Choose two.)
Correct answers: A and C
Explanation: A predicate must reference fields present in the dataset, and its design must not depend on values users cannot access reliably. Both points help prevent broken or unintended row-level access behavior.
Common mistake: Treating the predicate as a universal filter instead of a user-aware security expression.
Why the other options are wrong: B defeats the purpose of differentiated row-level access. D is false because predicates can use suitable standard or custom dataset fields.
What is the purpose of using the Dashboard Publisher feature in CRM Analytics?
Correct answer: C
Explanation: Dashboard Publisher supports delivery management by distributing dashboard information to users on a recurring basis.
Common mistake: Confusing dashboard delivery with data export, widget security, or filter interaction behavior.
Why the other options are wrong: A describes an integration or export use case. B concerns dashboard interaction logic. D requires access and security controls, not publishing.
What should you do in Einstein Discovery to improve a model’s performance when the data contains numerous irrelevant fields?
Correct answer: B
Explanation: Removing irrelevant fields reduces noise and helps the model focus on features that have meaningful predictive value.
Common mistake: Assuming that more columns always improve a model. Extra fields can add noise, increase complexity, and weaken interpretation.
Why the other options are wrong: A can worsen the original problem. C changes data types without proving relevance. D cannot replace deliberate review of unnecessary inputs.
When managing datasets in CRM Analytics, what should you do to ensure fields have user-friendly names and labels?
Correct answer: C
Explanation: XMD controls presentation metadata for dataset fields, including user-facing labels and formatting, without requiring the source field to be renamed.
Common mistake: Changing the source schema when the requirement is only to improve how a field appears in CRM Analytics.
Why the other options are wrong: A is not the proper direct mechanism for this display requirement. B transforms data rather than managing all presentation metadata. D changes Salesforce source configuration and may not provide the required dataset display behavior.
No. The exam has 60 scored questions and may include up to five additional unscored questions. The unscored questions are not identified during the exam.
No formal prerequisite is listed. However, practical Salesforce administration and CRM Analytics experience make the consultant scenarios much easier to interpret.
Compare its domain names and weights with the current Salesforce exam guide. Product terminology, release features, and maintenance requirements can change over time.
The source demo contains 10 questions, while this page displays five representative examples. Check the product listing for the exact number of questions included in the purchased package.
Check the listing for a downloadable sample or request one before checkout. Review readability, explanation depth, and coverage of all six exam domains.
No, unless the selected package explicitly says so. Practice materials, Salesforce training, and exam registration are normally separate purchases.
Confirm supported operating systems, browser requirements, activation limits, and offline access before purchasing. PDF and test-engine editions can have different requirements.
Review the product terms before downloading or activating anything. Confirm the update period, delivery method, refund deadline, activation restrictions, and process for reporting access issues.
Practice connecting business requirements to the correct data, security, dashboard, deployment, and predictive-modeling decisions.
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