Salesforce Analytics-Con-201 Questions For Guaranteed Success [2026]

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| Section | Weight | Objectives |
|---|
| Topic 1: Data Layer | 25% | - Identify the right data sources and data flows for CRM Analytics
- 1. Define datasets
- 2. Describe use cases for Data Prep (Dataflows and Recipes)
- 3. Describe how Salesforce objects map to CRM Analytics
- Implement and configure the right data transformations
- 1. Identify and resolve data sync issues
- 2. Use Data Prep actions
|
| Topic 2: Analytics Dashboard Design | 20% | - Design and build CRM Analytics dashboards
- 1. Describe the use cases for Visualforce and Lightning components
- 2. Apply design best practices
- 3. Use Lenses and Dashboards
|
| Topic 3: Analytics Dashboard Configuration | 25% | - Configure dashboards
- 1. Configure bindings
- 2. Apply query filters
- 3. Set up dynamic dashboards
|
| Topic 4: Security | 10% | - Determine security and sharing settings
- 1. Describe how Salesforce security is applied in CRM Analytics
- 2. Implement row-level security
|
| Topic 5: Einstein Discovery | 20% | - Implement Einstein Discovery
- 1. Integrate predictions into CRM Analytics dashboards
- 2. Configure and deploy Einstein Discovery models
- Describe Einstein Discovery concepts
- 1. Identify the use cases for Einstein Discovery
- 2. Interpret model results
- 3. Explain the model building process
|
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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q11-Q16):
NEW QUESTION # 11
Universal Containers is setting up a Sales Analytics app to track the sales performance across all regions. The role hierarchy is well-structured with separate branches for various global regions.
Viewer access has been provided to the regional sales reps based on the following roles.
* Regional Sales - APAC
* Regional Sales - EMEA
* Regional Sales - LATAM
* Regional Sales - NA
The CRM Analytics project team receives a request to extend Editor
access to the app to 20 'Superusers' across all regions.
What is the optimal solution?
- A. Create a new sharing rule for these users and assign it the Editor app access.
- B. Create a new public group with these users and assign it the Editor app access.
- C. Create new roles for these users and assign them the Editor app access.
Answer: B
NEW QUESTION # 12
The below image shows a numeric outcome being deployed (Regression).

Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
- A. Area Under Curve, R2 (R-squared)
- B. Root Mean Square Error, Minimum Square Error
- C. Area Under Curve, Confusion Matrix
Answer: B
Explanation:
In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:
* Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.
* Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors- essentially, the average squared difference between the estimated values and what is estimated.
These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model's predictions in real-world applications.
NEW QUESTION # 13
A consultant wants to understand what the important predictors are in a model.
Where is this information found?
- A. Model Settings
- B. Einstein Recommendations
- C. Model Deployment Wizard
Answer: A
Explanation:
The important predictors of a model in CRM Analytics can typically be found under theModel Settings. This area provides detailed information about the configuration and the inputs (predictors) used to train the model.
Insights into which predictors have the most significant impact on the model's outcomes can be gleaned from this section, enabling a deeper understanding of the model's internal workings and the factors driving predictions.
NEW QUESTION # 14
A CRM Analytics consultant is adding direct data into an existing recipe. When trying to click on the input node, data preview is taking a long time to show any results.
How should the consultant resolve this?
- A. Use the Filtered Rows sampling mode under Data Preview Sampling to filter records.
- B. Register the Salesforce object and use Filtered Rows under Data Preview Sampling.
- C. Update the Sample Size under Data Preview Sampling to 5,000 rows.
Answer: A
NEW QUESTION # 15
Cloud Kicks (CK) has a dashboard that is still showing 10 former account managers. CK has since expanded, and some have moved from their former roles. CK wants the dashboard to reflect current account managers and stay up to date as new team members join.
How should the CRM Analytics developer accomplish this?
- A. Build a report and get notified when a new account manager joins, and then edit the dashboard to add the account manager.
- B. Create @ Watchlist and when the number of account manager updates, update the dashboard to reflect the same.
- C. Use a Repeater Widget that updates automatically as the data flows through the system.
Answer: C
Explanation:
TheRepeater Widgetin CRM Analytics allows dynamic updates to be reflected on the dashboard automatically as data flows are updated. This means that as new account managers join or existing account managers change roles, the data in the widget will automatically reflect the latest information without manual updates. This is the most efficient way to keep the dashboard up-to-date with the current list of account managers.
Reference: The functionality ofRepeater Widgetsand their ability to dynamically update based on live data is covered in theWave Analytics Explorerdocumentation, particularly when dealing with team structures and dynamic datasets.
NEW QUESTION # 16
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