Salesforce CRM Analytics and Einstein Discovery Consultant Practice Exams
Last updated on Oct 21,2026- Exam Code: CRM Analytics and Einstein Discovery Consultant
- Exam Name: Salesforce Certified CRM Analytics and Einstein Discovery Consultant Exam
- Certification Provider: Salesforce
- Latest update: Oct 21,2026
Question #10
A user is able to access the dashboards, lenses, and datasets of a particular app but is unable to
change the name of the specific app.
What is causing the issue?
- A . The user does not have Manager access for that app.
- B . The app name cannot be changed once created.
- C . The user does not have Editor access for that app.
Correct Answer: A
A
Explanation:
In CRM Analytics, the ability to modify the name of an app or make other significant changes typically requires Manager access. This level of access is distinct from Editor or Viewer permissions, which may allow for modifications to contents within the app but not to the app’s core properties like its name. Here’s the reasoning:
Access Restrictions: Manager access is specifically designed to control structural changes within the app, including renaming the app, which is considered a higher privilege operation.
Role-Based Access Control: This ensures that only users with the necessary permissions can make significant changes, protecting the integrity and configuration of the app.
Ensuring users have the appropriate level of access based on their responsibilities is a fundamental aspect of managing security and functionality in CRM Analytics.
A
Explanation:
In CRM Analytics, the ability to modify the name of an app or make other significant changes typically requires Manager access. This level of access is distinct from Editor or Viewer permissions, which may allow for modifications to contents within the app but not to the app’s core properties like its name. Here’s the reasoning:
Access Restrictions: Manager access is specifically designed to control structural changes within the app, including renaming the app, which is considered a higher privilege operation.
Role-Based Access Control: This ensures that only users with the necessary permissions can make significant changes, protecting the integrity and configuration of the app.
Ensuring users have the appropriate level of access based on their responsibilities is a fundamental aspect of managing security and functionality in CRM Analytics.
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?
- A . Area Under Curve, R2 (R-squared)
- B . Area Under Curve, Confusion Matrix
- C . Root Mean Square Error, Minimum Square Error
Correct Answer: C
C
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.
C
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.
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?
- A . Area Under Curve, R2 (R-squared)
- B . Area Under Curve, Confusion Matrix
- C . Root Mean Square Error, Minimum Square Error
Correct Answer: C
C
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.
C
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.
Question #14
A manager at Cloud Kicks wants to separate and analyze accounts based on numeric information of its opportunity records. The data includes things like amount, quantity of products, contacts, and quotes.
How should the CRM Analytics consultant accomplish this?
- A . Bucket for each measure and then use a global bucket to segment accounts.
- B . Aggregate to summarize related data to account level.
- C . Cluster in the recipes and select the metrics used for clusterization.
Correct Answer: B
Question #15
What is the purpose of the CRM Analytics Dashboard Inspector?
- A . To view the total time required to run all queries.
- B . To automatically remove bottlenecks to make queries run faster.
- C . To see the final query for each widget along with the results.
Correct Answer: C
C
Explanation:
The CRM Analytics Dashboard Inspector is a powerful tool used to troubleshoot and optimize dashboards. Its primary function is to display the underlying SAQL (Salesforce Analytics Query Language) query executed for each widget. It helps users see the final query that is run and the corresponding results. This feature allows CRM Analytics consultants and developers to diagnose issues, optimize performance, and understand how data is being processed in the dashboard. While the Inspector helps view execution times and identify bottlenecks, it does not automatically resolve performance issues (which is why option B is incorrect). It simply provides visibility into query performance and execution details, allowing the user to make manual optimizations.
Reference: Detailed information on the Dashboard Inspector tool can be found under the "Building Lenses, Dashboards, and Apps in CRM Analytics" module and the Einstein Discovery training content within Salesforce documentation.
C
Explanation:
The CRM Analytics Dashboard Inspector is a powerful tool used to troubleshoot and optimize dashboards. Its primary function is to display the underlying SAQL (Salesforce Analytics Query Language) query executed for each widget. It helps users see the final query that is run and the corresponding results. This feature allows CRM Analytics consultants and developers to diagnose issues, optimize performance, and understand how data is being processed in the dashboard. While the Inspector helps view execution times and identify bottlenecks, it does not automatically resolve performance issues (which is why option B is incorrect). It simply provides visibility into query performance and execution details, allowing the user to make manual optimizations.
Reference: Detailed information on the Dashboard Inspector tool can be found under the "Building Lenses, Dashboards, and Apps in CRM Analytics" module and the Einstein Discovery training content within Salesforce documentation.
Question #16
A company realizes it has a lot of rich information around its cases, but unfortunately, most of this is unstructured/textual data. The company is exploring how to include some of this information in its case prioritization.
Which option within CRM Analytics should a consultant leverage?
- A . Bucket transformation in Recipes
- B . Cluster transformation in Recipes
- C . Detect Sentiment transformation in Recipes
Correct Answer: C
C
Explanation:
For a company with a wealth of unstructured textual data in their cases, the "Detect Sentiment" transformation within CRM Analytics Recipes is a crucial tool. This transformation analyzes the sentiment of the text data―whether it’s positive, neutral, or negative―and this insight can be highly valuable in case prioritization processes.
Here’s why this transformation is useful:
Insight into Customer Sentiments: By detecting sentiment, the company can prioritize cases based on the urgency and emotional tone expressed in the text, which might indicate customer dissatisfaction or urgency.
Automation and Efficiency: Automatically categorizing cases based on sentiment can streamline workflows and ensure that critical cases are handled promptly.
Enhanced Customer Service: Responding to negative sentiments swiftly can improve customer satisfaction and potentially mitigate issues before they escalate.
C
Explanation:
For a company with a wealth of unstructured textual data in their cases, the "Detect Sentiment" transformation within CRM Analytics Recipes is a crucial tool. This transformation analyzes the sentiment of the text data―whether it’s positive, neutral, or negative―and this insight can be highly valuable in case prioritization processes.
Here’s why this transformation is useful:
Insight into Customer Sentiments: By detecting sentiment, the company can prioritize cases based on the urgency and emotional tone expressed in the text, which might indicate customer dissatisfaction or urgency.
Automation and Efficiency: Automatically categorizing cases based on sentiment can streamline workflows and ensure that critical cases are handled promptly.
Enhanced Customer Service: Responding to negative sentiments swiftly can improve customer satisfaction and potentially mitigate issues before they escalate.
Question #17
Which statement best describes how to ensure CRM Analytics dashboards are easily used across both desktop and mobile devices?
- A . Create multiple layouts and reorder all the widgets so that they fit nicely within the new default width
- B . Create multiple layouts and allow CRM Analytics to automatically select and organize dashboard contents to be optimal for the device type.
- C . Create multiple layouts, ensure the layout selectors match the device, and resize/hide widgets to ensure the content is appropriate for the device screen size.
Correct Answer: C
C
Explanation:
To ensure that CRM Analytics dashboards are optimally usable on both desktop and mobile devices, creating multiple layouts tailored to each device type is crucial. Here’s why Option C is the best approach:
Device-Specific Layouts: By creating specific layouts for each device type, you ensure that the dashboard contents are presented in a manner best suited to the screen size and interaction model of the device.
Layout Selectors: These are used to automatically display the appropriate layout based on the device accessing the dashboard, enhancing user experience without manual intervention.
Widget Customization: Resizing or hiding certain widgets for specific device layouts ensures that the dashboard remains clean, uncluttered, and easy to navigate, regardless of the device used.
C
Explanation:
To ensure that CRM Analytics dashboards are optimally usable on both desktop and mobile devices, creating multiple layouts tailored to each device type is crucial. Here’s why Option C is the best approach:
Device-Specific Layouts: By creating specific layouts for each device type, you ensure that the dashboard contents are presented in a manner best suited to the screen size and interaction model of the device.
Layout Selectors: These are used to automatically display the appropriate layout based on the device accessing the dashboard, enhancing user experience without manual intervention.
Widget Customization: Resizing or hiding certain widgets for specific device layouts ensures that the dashboard remains clean, uncluttered, and easy to navigate, regardless of the device used.
Question #18
Universal Containers (UC) is using CRM Analytics to create two datasets.
* Dataset A: Contains a list of activities with an "activityID" dimension and a "userID" dimension
* Dataset B: Contains a list of users with a "userID" dimension
UC wants to delete all activities from Dataset A related to users in Dataset B.
How should the CRM Analytics consultant help UC achieve this?
- A . Use the combination of recipe transformations: "join" and “filter”.
- B . Use the recipe node "delete" and set "userID" as the deletion ID.
- C . Use the “update” transformation and utilize the "filter" node.
Correct Answer: A
A
Explanation:
In CRM Analytics, when dealing with two datasets, such as Dataset A (activities) and Dataset B (users), and you want to delete records from Dataset A based on the users listed in Dataset B, you would typically use a combination of a join and filter transformation in a recipe. The join transformation allows you to combine data from both datasets based on the shared userID dimension, and the filter transformation would then be used to delete or exclude any activities from Dataset A that are associated with the users from Dataset B. This approach ensures that only relevant activities remain in Dataset A after filtering out the unwanted ones.
Reference: CRM Analytics Recipes: Join and Filter Transformations
A
Explanation:
In CRM Analytics, when dealing with two datasets, such as Dataset A (activities) and Dataset B (users), and you want to delete records from Dataset A based on the users listed in Dataset B, you would typically use a combination of a join and filter transformation in a recipe. The join transformation allows you to combine data from both datasets based on the shared userID dimension, and the filter transformation would then be used to delete or exclude any activities from Dataset A that are associated with the users from Dataset B. This approach ensures that only relevant activities remain in Dataset A after filtering out the unwanted ones.
Reference: CRM Analytics Recipes: Join and Filter Transformations
Question #19
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 . Update the Sample Size under Data Preview Sampling to 5,000 rows.
- B . Register the Salesforce object and use Filtered Rows under Data Preview Sampling.
- C . Use the Filtered Rows sampling mode under Data Preview Sampling to filter records.
Correct Answer: C
Question #20
In Data Alerts, a field named Region is 96.5% North America. The consultant believes that this is an important field since the majority of sales is in the North American market.
What is the appropriate action?
- A . Choose the recommended action “keep only North America” and exclude all of the other rows.
- B . Understand if client should only "focus on North America” or drop the field, as it does not have valuable information.
- C . Append more rows of data with "other values of Region” to balance the dataset properly.
Correct Answer: B