CRM Analytics and Einstein Discovery Consultant Exam Practice Questions
The CRM Analytics and Einstein Discovery Consultant Exam is designed for Salesforce professionals who specialize in data analytics and predictive insights using Salesforce’s powerful CRM Analytics platform and Einstein Discovery tools. This certification validates your ability to design, build, and deliver analytics solutions that help businesses make smarter, data-driven decisions.
Why Pursue This Certification?
With the explosive growth of data, companies increasingly rely on analytics to understand customer behavior, forecast trends, and optimize operations. Salesforce’s CRM Analytics combines data integration, visualization, and advanced AI-powered predictions with Einstein Discovery. Earning this credential proves you have the skills to transform raw data into actionable business intelligence.
Exam Focus Areas
Candidates will be tested on various topics, including:
Designing and implementing CRM Analytics dashboards and datasets
Creating data recipes and transformations to prepare and blend data
Utilizing Einstein Discovery to build predictive models and interpret insights
Applying best practices for security, sharing, and governance in analytics
Integrating predictive insights into Salesforce Lightning pages
Scheduling dataflows and managing dataset refreshes
Troubleshooting and optimizing analytics solutions for performance
Skills You Will Gain
Preparing for this exam equips you with:
Expertise in data preparation using CRM Analytics recipes
Advanced dashboard design with interactive filters and widgets
Understanding of AI-driven analytics and machine learning models in Einstein Discovery
Ability to interpret model results, explain predictions, and recommend actionable steps
Hands-on experience integrating analytics insights directly into Salesforce workflows
How to Prepare
Success requires hands-on practice with Salesforce CRM Analytics Studio and Einstein Discovery. It’s recommended to:
Explore real-world datasets to create reports and dashboards
Build and test predictive models in Einstein Discovery
Study official Salesforce resources, trailheads, and community forums
Practice with multiple-choice questions and mock exams to strengthen your understanding
Career Impact
Certified CRM Analytics and Einstein Discovery Consultants are highly sought after in roles like Business Analyst, Data Analyst, Analytics Consultant, and Salesforce Administrator. This certification can open doors to advanced career opportunities and higher earning potential by demonstrating your analytics expertise.
Sample Questions and Answers
1. Which feature in CRM Analytics allows you to automate data refresh and notify users when dashboards are updated?
A) Dataflow
B) Data Recipes
C) Scheduled Jobs
D) Einstein Discovery
Answer: C) Scheduled Jobs
Explanation: Scheduled Jobs enable you to automate data refreshes and send notifications when dashboards are updated, ensuring users always see current data.
2. What is the primary purpose of a Data Recipe in CRM Analytics?
A) To transform and clean data using a graphical interface
B) To create predictive models
C) To build dashboards
D) To schedule data imports
Answer: A) To transform and clean data using a graphical interface
Explanation: Data Recipes let you visually prepare and transform datasets without writing code, making data prep accessible.
3. Which Salesforce tool integrates directly with CRM Analytics for building predictive models without requiring code?
A) Tableau CRM
B) Einstein Discovery
C) Salesforce Flow
D) Salesforce Reports
Answer: B) Einstein Discovery
Explanation: Einstein Discovery provides AI-powered predictive analytics integrated with CRM Analytics, enabling users to build models with no coding.
4. In Einstein Discovery, what is the significance of ‘explanations’ generated with predictions?
A) They describe why a model made a certain prediction
B) They provide visualizations of the dataset
C) They detail the data preparation steps
D) They schedule model retraining
Answer: A) They describe why a model made a certain prediction
Explanation: Explanations clarify the key factors driving a prediction, increasing user trust in the model.
5. What type of dataset is best suited for CRM Analytics when you need to analyze sales pipeline stages over time?
A) Snapshot dataset
B) Transactional dataset
C) Time-series dataset
D) Metadata dataset
Answer: C) Time-series dataset
Explanation: Time-series datasets track changes over time, ideal for monitoring sales pipeline stages and trends.
6. When building a dashboard in CRM Analytics, which widget would you use to show a geographical distribution of customers?
A) Table widget
B) Line chart widget
C) Map widget
D) KPI widget
Answer: C) Map widget
Explanation: The Map widget visualizes data geographically, showing customer locations and distributions.
7. What does the term ‘dataflow’ refer to in CRM Analytics?
A) The process of moving and transforming data from sources to datasets
B) The visualization of data in dashboards
C) The automation of report generation
D) The user access control system
Answer: A) The process of moving and transforming data from sources to datasets
Explanation: Dataflow pipelines extract, transform, and load data into CRM Analytics datasets.
8. Which of the following best describes the ‘Explain’ feature in Einstein Discovery?
A) It helps users understand the dataset’s metadata
B) It identifies the most important variables influencing predictions
C) It automates dashboard creation
D) It schedules prediction refreshes
Answer: B) It identifies the most important variables influencing predictions
Explanation: The Explain feature highlights key drivers affecting model outcomes to help users interpret predictions.
9. What is a ‘lens’ in CRM Analytics?
A) A visualization of data created from a dataset
B) A data integration tool
C) A machine learning model
D) A report builder
Answer: A) A visualization of data created from a dataset
Explanation: Lenses are saved data visualizations or queries that users can explore and include in dashboards.
10. Which CRM Analytics feature allows users to perform self-service exploration of data without a developer?
A) Dataflow
B) Data Recipes
C) Lenses
D) Einstein Discovery
Answer: C) Lenses
Explanation: Lenses enable business users to explore datasets interactively, building queries and visualizations.
11. What is the role of a ‘dashboard filter’ in CRM Analytics?
A) To restrict user access to data
B) To allow users to dynamically adjust dashboard views based on criteria
C) To schedule data refreshes
D) To automate report sharing
Answer: B) To allow users to dynamically adjust dashboard views based on criteria
Explanation: Filters let users change dashboard parameters, refining the data they see.
12. In Einstein Discovery, what is a ‘story’?
A) A set of dashboards showing data trends
B) An automated explanation and recommendation report generated by the model
C) A dataflow process
D) A scheduled job
Answer: B) An automated explanation and recommendation report generated by the model
Explanation: A story summarizes model insights and suggests actions based on predictions.
13. What is the recommended way to improve model accuracy in Einstein Discovery?
A) Increase the number of dashboards
B) Use more relevant and high-quality data
C) Add more users to the org
D) Remove data recipes
Answer: B) Use more relevant and high-quality data
Explanation: Better data quality and relevance lead to improved model performance and accuracy.
14. How does CRM Analytics support multi-source data integration?
A) By only using Salesforce data
B) By importing data from external databases, spreadsheets, and Salesforce objects
C) By using only dataflows
D) By automating report creation
Answer: B) By importing data from external databases, spreadsheets, and Salesforce objects
Explanation: CRM Analytics can connect to multiple data sources for comprehensive analysis.
15. What does a ‘data recipe’ allow you to do that a ‘dataflow’ does not?
A) Schedule data refresh
B) Clean and prepare data with a no-code interface
C) Create dashboards
D) Build predictive models
Answer: B) Clean and prepare data with a no-code interface
Explanation: Data recipes provide an easy visual interface for data prep, whereas dataflows require JSON coding.
16. What is the best practice for sharing CRM Analytics dashboards securely?
A) Share links publicly on social media
B) Use Salesforce permission sets and sharing settings to control access
C) Email dashboards to all users
D) Export dashboards as PDFs and upload to public servers
Answer: B) Use Salesforce permission sets and sharing settings to control access
Explanation: Use built-in Salesforce security to ensure only authorized users see sensitive data.
17. Which type of visualization is most effective for showing trends over time in CRM Analytics?
A) Bar chart
B) Line chart
C) Pie chart
D) Heat map
Answer: B) Line chart
Explanation: Line charts clearly illustrate changes and trends over time periods.
18. In Einstein Discovery, what is a ‘prediction’?
A) A calculated insight about future events based on historical data
B) A visual dashboard element
C) A data preparation step
D) A user permission setting
Answer: A) A calculated insight about future events based on historical data
Explanation: Predictions forecast outcomes by analyzing past data patterns.
19. What is the purpose of ‘Recipe Outputs’ in CRM Analytics?
A) To define the final dataset after transformations in a data recipe
B) To schedule data refresh
C) To create predictive models
D) To automate dashboard updates
Answer: A) To define the final dataset after transformations in a data recipe
Explanation: Recipe outputs are datasets ready for exploration or dashboard use after cleaning.
20. How does CRM Analytics handle large datasets to maintain dashboard performance?
A) By limiting dataset size to under 1000 rows
B) Using efficient indexing, aggregation, and data snapshotting techniques
C) By disabling user filters
D) By exporting data offline only
Answer: B) Using efficient indexing, aggregation, and data snapshotting techniques
Explanation: Performance optimization techniques ensure smooth interaction with large datasets.
21. What is the function of ‘Einstein Discovery Explorer’?
A) To create Salesforce reports
B) To visually explore datasets and uncover insights using AI
C) To build dashboards only
D) To schedule data imports
Answer: B) To visually explore datasets and uncover insights using AI
Explanation: Explorer helps users analyze data and find trends and outliers with AI assistance.
22. Which of the following can be automated using Einstein Discovery?
A) Predictive model building and scoring
B) User management
C) Dataflow editing
D) Dashboard creation
Answer: A) Predictive model building and scoring
Explanation: Einstein Discovery automates building predictive models and generating scores.
23. When integrating Einstein Discovery with CRM Analytics, what is a key benefit?
A) Ability to embed predictive insights directly into CRM Analytics dashboards
B) Increased user licenses
C) Faster dashboard loading times
D) More storage for raw data
Answer: A) Ability to embed predictive insights directly into CRM Analytics dashboards
Explanation: Integration allows users to see AI predictions alongside traditional analytics seamlessly.
24. What kind of data is best suited for Einstein Discovery modeling?
A) Data with clear outcomes or targets for prediction
B) Raw, uncleaned data
C) Only external database data
D) Purely textual data without numerical attributes
Answer: A) Data with clear outcomes or targets for prediction
Explanation: Einstein Discovery requires labeled data with known outcomes for supervised learning.
25. Which CRM Analytics feature allows combining multiple datasets into one?
A) Data Recipe Join
B) Dataflow Output
C) Lens Explorer
D) Dashboard Filter
Answer: A) Data Recipe Join
Explanation: Data Recipe Join merges datasets by common keys for combined analysis.
26. What is the main function of ‘Einstein Discovery Predictions’ in CRM Analytics dashboards?
A) To display historical data trends
B) To provide AI-based predictions alongside traditional analytics
C) To filter dashboard data
D) To schedule data refresh
Answer: B) To provide AI-based predictions alongside traditional analytics
Explanation: Predictions add AI insights to dashboards, enabling data-driven decisions.
27. How do you improve user adoption of CRM Analytics dashboards?
A) Provide training and customize dashboards to user roles and needs
B) Limit dashboard access
C) Disable interactivity
D) Share dashboards only via PDF
Answer: A) Provide training and customize dashboards to user roles and needs
Explanation: User-friendly dashboards and proper training encourage adoption and engagement.
28. Which of these best describes ‘predictive scoring’ in Einstein Discovery?
A) Assigning a score to new data based on a trained model’s prediction
B) Scoring user interactions with dashboards
C) Ranking datasets by size
D) Measuring dashboard performance
Answer: A) Assigning a score to new data based on a trained model’s prediction
Explanation: Predictive scoring applies model results to new records to estimate outcomes.
29. Which CRM Analytics component controls how users interact with dashboards?
A) Dashboard Actions
B) Dataflows
C) Data Recipes
D) Einstein Discovery Stories
Answer: A) Dashboard Actions
Explanation: Actions like filters, drilldowns, and bindings enable dynamic user interaction.
30. What is the best way to validate an Einstein Discovery model before deploying it?
A) Review model accuracy metrics, explanations, and test with a validation dataset
B) Deploy immediately and monitor user feedback
C) Use only training data results
D) Share the model with all users without review
Answer: A) Review model accuracy metrics, explanations, and test with a validation dataset
Explanation: Validating model performance ensures reliability and trustworthiness before deployment.
