Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Questions and Answers

In Salesforce Tableau CRM, dashboards can be shared with others within your organization using the "Share" icon located on the dashboard itself. This feature allows users to:

• Set up specific sharing options to control who can view or edit the dashboard.
• Post a screenshot of the dashboard directly to Chatter to facilitate discussion or collaboration.
• Obtain a URL that can be shared with other users who have the necessary permissions to access the dashboard.
• Download a screenshot of the dashboard for use in presentations or reports outside of Salesforce.

Einstein Discovery presents insights based on their importance in explaining the variation in the outcome variable, in descending order. This approach prioritizes insights that have the most significant impact on the outcome, helping users to focus on the most influential factors first. By sorting insights in this manner, Einstein Discovery ensures that users can quickly identify and address the key drivers of their business metrics, facilitating more effective decision-making based on the most critical data points. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_story_overview.htm &type=5

If an administrator is unable to upload a custom geoJSON file via the user interface, the recommended approach is:

• C: Upload the geoJSON via the API because it is not a function in the UI. This method allows for the integration of custom geoJSON maps into Salesforce dashboards, even when UI support is not available.

In Tableau CRM's Insights list, insights are ranked according to their statistical significance. This means that the insights considered most impactful or relevant based on the underlying statistical analysis are presented first. This ranking helps users quickly identify the most critical insights derived from their data.

For Einstein Discovery in Salesforce Tableau CRM, the maximum number of new stories that can be created per organization per day is capped at 20. This limit helps manage the load on the system, ensuring stability and performance across the platform.

The steps in the data exploration process in Salesforce Tableau CRM are as follows:

• View: Initially viewing the data to understand what is available and identify areas of interest.
• Explore: Digging deeper into the data to discover patterns, anomalies, and insights.
• Refine: Adjusting the exploration by filtering, sorting, or enhancing the data visualization for clearer insights.
• Save: Saving the state of your exploration to revisit or continue working on later.
• Share: Sharing your findings with others in the organization, either through direct dashboard access, downloadable reports, or URLs.

To meet the security requirements of allowing only a subset of users to access the dataset and ensuring they only see data relevant to their region, the consultant can:

• Use a security predicate in the dataset so users can see only rows belonging to their respective regions (B): This involves configuring row-level security using security predicates based on the UserRegion field, which filters data dynamically based on the user's region.
• Save the dataset under a new application and give "viewer" access to a public group containing the list of users allowed to see the dataset (D): This method segregates access at the application level, allowing only designated users to view the dataset within that application.

When extending the Sales Operations app to distribute it as an Einstein Analytics template app, the correct endpoint to post the folderSource ID (as shown in the JSON snippet provided) is "/services/data/vXX.X/wave/templates". This endpoint is used for managing template apps in Einstein Analytics, allowing the consultant to package and distribute standardized analytical content across different Salesforce instances or to different user groups within the organization.

To allow users who belong to an account team to see all the opportunities associated with that account in Einstein Analytics, the recommended actions are:

• Apply sharing inheritance: This ensures that the security settings and visibility of the Account object are inherited by related records, like Opportunities, making it easier to manage access based on existing Salesforce CRM security models.
• In the dataflow, extract the AccountTeamMember object and augment it with the Opportunity object using 'AccountId' as the join field: This method allows the consultant to directly link team members to the accounts and their opportunities, ensuring that visibility in Einstein Analytics mirrors the CRM's team-based permissions.

These approaches leverage Salesforce's inherent security features and ensure data visibility is consistent with user roles and relationships defined in the CRM system.

In the formulas used within a Compare Table in Tableau CRM, you can refer to other columns by using their names. This method provides clarity and maintains consistency in the formula, making it easier to understand and modify if necessary. Referring to columns by their names, as opposed to numbers or letters, helps prevent errors and improves the manageability of complex analytics formulas. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_dashboard_widgets_compare_table_advanced.htm &type=5

Before releasing an updated dashboard to production, the best practice is to remove redundant steps to ensure dashboard performance. This optimization helps in minimizing the load time and resource consumption, particularly important after iterative modifications such as adding or removing charts.

In Tableau CRM, the default number of rows shown in a values table is 50. This default setting helps manage display and performance efficiently while providing an adequate snapshot of data for initial analysis.

When writing a SAQL query to predict sales per product family using the timeseries SAQL statement, the consultant must:

• Investigate which prediction model is best for the data available (B): Choosing the right prediction model is critical to ensuring the accuracy and relevance of the forecast. Different models may perform better based on the characteristics of the data.
• Validate that there is enough data in the dataset to make a good prediction (C): Ensuring a sufficient amount of quality data is available is essential for making reliable predictions. This includes having enough historical data points and representative data.

In Tableau CRM, bindings are used to dynamically control the data and metadata in your dashboard components based on user interactions. The two main types of bindings are Results bindings, which allow you to use the results of one query as input for another component, and Selections bindings, which let you use the user's selections in one component to filter or alter the data in another.

Creating an Einstein Discovery story begins with selecting a dataset in Tableau CRM. Users can navigate to the desired dataset and initiate the story creation process by selecting the "Create Story" option. This feature allows users to explore data automatically, uncovering patterns and predictive insights that help in making data-driven decisions. The process does not involve copying texts or uploading presentations but rather utilizes the existing datasets to generate meaningful and actionable insights. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_create_story.htm &type=5

The maximum number of concurrent API Analytics calls per organization in Salesforce Tableau CRM is 200. This limit helps to ensure that the system can handle multiple API requests efficiently without degrading the performance for users across the organization.

To open the Actions menu for a particular case shown in a dashboard in Salesforce Tableau CRM:

• B: Hover over the case number column of the values table widget and click the arrow that appears. This action will display the menu related to that specific case, offering various actions that can be performed directly from the dashboard.

When clients express disappointment that Einstein Discovery only detected known patterns, an appropriate response is to highlight the efficiency benefits, specifically how Einstein significantly reduces the time to insight compared to traditional methods. This emphasizes the value of using Einstein Discovery to quickly validate known patterns and potentially explore nuances within those patterns more swiftly than through manual analysis.

The correct set of statements that generate a monthly amount on a cumulative basis annually is Option B. This option likely includes cumulative sum functions applied within a grouped statement, typically done with a grouping by 'Month' and an annual reset. Such SQL-like syntax is commonly used in Tableau CRM for time-based cumulative calculations.

To determine whether a subscription is new or a renewal when working with Closed Won Opportunity records in Salesforce that do not explicitly specify this information, the appropriate data transformation is ComputeExpression. This transformation allows you to create a new field or modify existing ones based on a specified expression. For instance, the consultant can use logic comparing the account's first purchase date to the opportunity close date to ascertain whether the subscription is a renewal.

In Tableau CRM, a Compare Table allows you to segment data into different groupings for comparison. You can have up to four groupings in a Compare Table. This feature enables users to analyze and visualize data by comparing different segments side by side within the same table, facilitating a clearer understanding of how various groups perform against each other across multiple metrics. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_dashboard_widgets_compare_table.htm &type=5

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When you disable Analytics in Salesforce, the system does not automatically remove user permissions from the permission sets. These permissions remain intact and are not affected by the disabling of the service. Consequently, if you re-enable Analytics at a later time, there is no need to redefine these permission sets as the original configurations remain in place. This behavior ensures that administrators can temporarily disable and re-enable Analytics without redoing the setup for user access, making it efficient to manage without risking data security or access errors. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_enable_features.htm &type=5

https://blog.bessereau.eu/assets/pdfs/bi_admin_guide_setup.pdf

Using Chart Markers in a bar chart is an effective way to dynamically emphasize certain values, making it easier for users to focus on key information. Chart markers can be configured to highlight data points based on specific conditions or thresholds, such as highlighting values above or below a certain number, or showcasing changes over time. This visual differentiation aids in

To build a table in a Sales Manager dashboard and include a summary as the first line in a compare table:

• A: Use the Show Summary option in the compare table. This feature automatically calculates and displays a summary row at the top of the compare table, providing an aggregated view of the data points included in the table.

When Salesforce Analytics runs a dataflow that accesses Salesforce objects, it uses the Integration User. This is a system user configured to perform back-end operations that require interacting with Salesforce data, independent of any specific human user. This setup ensures that data operations such as dataflows are consistent and not tied to the permissions or roles of individual users, which could vary and affect the execution of such tasks. Reference: Salesforce Help - Dataflows and Integration User

To load data from an external, on-premise Oracle database into Einstein Analytics, the recommended options are:

• Use the Analytics External Data API: This API facilitates the integration of external data sources with Einstein Analytics, allowing for automated data uploads.
• Load the data as a CSV file: This method involves extracting data from the Oracle database into a CSV format and then uploading it to Einstein Analytics.

The issue of the VP of Sales seeing "No results found" while others can view data in the dataset suggests a problem with access permissions. Two likely causes are: A) The VP’s Salesforce profile may lack read permissions on some crucial fields of the Opportunity__c custom object, which could prevent viewing data where those fields are essential. B) Security predicates at the dataset level might be set up in a way that restricts data visibility for the VP, perhaps due to specific conditions that exclude higher hierarchy levels.

To build a lens showing the total number of Opportunities and Cases per Account using two separate datasets, the consultant can:

• D: Use a SAQL query to group and join the "OpportunityWithAccount" and "CaseWithAccount" datasets then generate the results. This method involves writing a SAQL query that groups the data by Account and aggregates the number of Opportunities and Cases, providing a unified view of the data across both datasets.

* template-info.json manages all elements of your template, including metadata information about the template, the Analytics objects that define dashboards and lenses, and the other files that are part of the template.* ui.json manages the configuration wizard that drives app creation. It defines the number of wizard pages, the order of wizard questions, and any messages you want the user to see.* variables.json contains all template variables, including text for wizard questions and specifications for the answers. Variables also define conditional questions. For example, you may want some questions to appear in the wizard only if an org contains certain data. Or you might want to add more specific questions based on the answers to more general wizard questions.* template-to-app-rules.json defines the rules the template follows. For example, you could define a rule that specifies that if an org doesn’t use certain Salesforce objects, the app won’t refer to them in dashboards or include them in the dataflow. Rules also define how variables are handled. For example, if the wizard asks which fields to include in filters for accounts, template-to-app-rules.json determines how that choice is reflected in dashboards.

alesforce Tableau CRM uses two internal users—Integration User and Security User—to manage access to Salesforce data for analytics purposes. The Integration User is used to extract data from Salesforce objects and fields during dataflow jobs, ensuring that data movements are secure and compliant with the organization's access policies. The Security User's permissions are employed when querying datasets with row-level security based on the User object, allowing for secure and appropriate data access based on user roles and permissions.

Assigning any Analytics permission set to users in a Salesforce org does not automatically assign the Analytics Platform permission set license. The license must be explicitly assigned to users before they can utilize the features enabled by the permission sets. This ensures that only authorized users have access to powerful analytics capabilities.

In Salesforce, access to data can be configured at several levels, including Organization, Records, and Objects. However, Page Layouts is not a level at which access to data is configured. Page Layouts determine the arrangement of fields, related lists, and other elements on a record's page but do not control data access or security settings. Access controls are managed through settings at other specified levels such as profiles, roles, and sharing rules. Reference: Salesforce Help - Data Security

In Salesforce Tableau CRM (Einstein Analytics), the visibility of data in a dataset like the Seed Bank Orders is controlled by the app manager within the context of the app to which the dataset has been added. Therefore, a project team member with the Viewer role in the Mosaic Seed Bank project team can see only the rows in the dataset that are designated by the App Manager. This access is configured to ensure data security and relevancy based on user roles and permissions. Reference: Salesforce Help - Dataset Security

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To analyze data from a CSV file in Einstein Discovery, the correct procedure involves importing the CSV file into an Einstein Analytics dataset and subsequently creating a story from this dataset. This method allows you to fully leverage Einstein Discovery's capabilities to analyze and derive insights from the data presented in the CSV file.

The best connection mode for Universal Containers, given their need to sync updated and new records from the Order object weekly, including updates to formula fields, is Periodic Full Sync. This mode will:

• Periodically sync all records from the source object (in this case, weekly).
• Ensure that all fields, including formula fields that may calculate values based on other periodically updated fields, are current in the destination system.

This mode is especially suitable when data completeness and accuracy are crucial, and where changes in the dataset need to reflect consistently, including computed or derived fields.

The correct operation to use with the Analytics External Data API to upload monthly target data, which includes replacing existing entries with new data each month, is:

• C: Overwrite. This operation ensures that each upload replaces the existing dataset entirely, which is necessary for scenarios where the target data changes every month, and historical precision needs to be maintained.

To meet the requirement where sales roles have varying levels of access to opportunities based on their role hierarchy and team membership, the best approach is:

• D: Use the flatten operation on the role hierarchy and create a multivalue attribute called 'ParentRoleIDs' on the opportunity dataset and apply the following security predicate: 'ParentRoleIDs' == "$User.UserRoleId" || 'TeamMember.Id' == "$User.Id" || 'Ownerld' == "$User.Id". This method allows the use of dynamic role hierarchy data to control visibility effectively, ensuring that each role has the appropriate level of access according to their position and team membership in the organization.

The appropriate action when Einstein Discovery surfaces an unexpected pattern is to determine if the pattern is a data issue or a new insight. This approach involves verifying the integrity and accuracy of the data, and understanding whether the pattern represents a meaningful insight into the business operations or an anomaly caused by data issues.

In designing a dashboard for a Senior Sales Business Analyst that involves fiscal year product opportunities: D. Use charts to help users ask questions, not illustrate a conclusion. This approach encourages interactive exploration and deeper analysis of the data, enabling users to discover insights rather than just viewing predetermined outcomes. It supports a more analytical approach to data interpretation, which is essential in complex scenarios like fiscal year analysis. Reference: Salesforce Documentation on Effective Dashboard Design

https://help.salesforce.com/articleView?id=bi_dashboard_build_tips.htm &type=5

In Tableau CRM, recipes allow users to create new datasets from various sources. The sources that can be utilized include:

• A. Existing datasets: Recipes can combine or transform data from datasets that have been previously created or imported into Tableau CRM.
• B. Records from a Salesforce object: Direct extraction of data from Salesforce objects into a new dataset via a recipe is supported, allowing users to leverage live Salesforce data for further analysis.

For a dashboard that needs to visualize Closed Won opportunities by different time groupings (month, quarter, year) using a single chart, the appropriate action is: D. Use selection binding to update the grouping in the chart. This method allows the chart to dynamically change the grouping based on user selection, effectively switching between month, quarter, or year views within the same visual representation. Reference: Salesforce Documentation on Dynamic Dashboards

The likely missed step in the deployment process of a Tableau CRM analytic app that includes a recipe and several datasets and dashboards, where the datasets aren't appearing post-deployment, is that the recipe needs to be run. Here's what should be done:

• Navigate to the Tableau CRM Analytics Studio.
• Locate the deployed recipe within the app.
• Manually initiate the recipe to run, or verify if it's scheduled to run automatically. If not, set up a schedule.
• Ensure that once the recipe runs, it processes and creates the datasets as designed.

This step is crucial because recipes are responsible for transforming data and generating the datasets needed for the dashboards.

The Service Analytics Overview dashboard in Tableau CRM is designed to provide comprehensive insights into various aspects of service operations. It includes:

• Key metrics such as open cases, average time to close, first contact resolution, and customer satisfaction, offering a quick overview of service efficiency.
• Drill-down capabilities into more detailed dashboards like agent performance, channel review, and telephony metrics, enabling deeper analysis.
• Availability on both desktop and mobile platforms, ensuring accessibility across different devices.
• It serves as an excellent starting point for service analytics, providing a holistic view of the service department’s performance.

This dashboard is a central tool for managing and analyzing service operations effectively, helping organizations to optimize their customer service strategies. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_service_analytics.htm &type=5