Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant Dumps

An Analytics app in Salesforce is a collection of datasets, dashboards, lenses, and a dataflow. These components are bundled together within an app to provide a comprehensive analytics solution tailored to specific business needs or project requirements. The app facilitates organized access to related analytics assets, making it easier for users to navigate and utilize data for decision-making and insights. Reference: Salesforce Help - Understanding Analytics Apps

The widget property that allows a consultant to restrict the view to a domain bounded by the values entered is "Add Custom Range." This property enables setting specific range limits on the data displayed in the widget, effectively controlling the scope of data visualization to ensure focus on relevant metrics.

The configuration wizard in Salesforce Tableau CRM Einstein Discovery asks questions about various measurements that are critical for understanding and analyzing customer interactions and service effectiveness, which include:

• A: Case duration
• B: Customer geography
• C: Customer satisfaction
• D: Case resolution
• E: All of the above

In Einstein Discovery stories, insights show how different variables, or combinations of variables, explain the variation of the outcome variable. The outcome variable is the target variable whose behavior and influencing factors are being analyzed and predicted by the model. Insights in the story will help understand how each explanatory variable and their interactions impact the outcome, which is central to predictive analytics and machine learning in Einstein Discovery. Reference: Salesforce Help - Einstein Discovery Insights

When you configure the story, you tell Einstein Discovery to maximize/minimize the variable. The variable is 'outcome variable' in your story

In Einstein Discovery, it is not necessary to remove all potential collinear fields before executing the build story because:

• B: No. Although it is ideal to eliminate collinearity as soon as possible, Einstein will give a warning post-build and the ridge regression will prevent collinearity from over-fitting. Einstein Discovery includes mechanisms such as ridge regression to manage collinearity, which helps prevent the model from over-fitting even when collinear variables are present.

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

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In Tableau CRM, each run of a recipe counts towards the 24-hour rolling recipe run limit, regardless of the duration of each recipe. Therefore, if all three recipes (myRecipeOne, myRecipeTwo, and myRecipeThree) are executed, all three count towards the limit. This is crucial for managing system resources and ensuring optimal performance of the analytics environment.

Two benefits of designing using the "Progressive Disclosure" principle are:

• A: Improved ease of use for end users. By only displaying essential information initially and allowing users to access more detailed data as needed, dashboards become less cluttered and more user-friendly.
• D: Better dashboard performance. This design principle can also lead to better performance as less data is processed and rendered initially, speeding up load times and responsiveness.

To write predictions back to the Predicted Amount field in the Opportunity object across all records, the best approach is to enable a Bulk Scoring Job under the Prediction Definition. This feature allows for the bulk processing of records to update the predictive field based on the latest model predictions. Here are the steps:

• Navigate to the Prediction Definitions in Tableau CRM.
• Select the prediction model applied to the Opportunity object.
• Configure the settings to specify the Predicted Amount field as the target for the output.
• Enable and configure the Bulk Scoring Job to run at desired intervals (e.g., daily, weekly), depending on the business requirement.

This process ensures that all Opportunity records are scored consistently with the most current predictions, keeping the Predicted Amount field updated.

For Benito to build and edit a dashboard using the Seed Bank dataset in the Einstein Analytics app, the admin needs to do the following two things:

• Grant Benito access to the specific app that contains the Seed Bank dataset. This ensures he can access the necessary dataset within the confines of the designated app.
• Assign him a permission set that includes the "Create and Edit Analytics Dashboards" permission. This permission is crucial for enabling Benito to not only view but also create and modify dashboards within the app.

This dual requirement ensures both data accessibility and the ability to perform required operations on the dashboard. Reference: Salesforce Help - Permission Sets

In Tableau CRM (formerly Einstein Analytics), a dataset recipe allows users to combine data from different datasets, enriching the data through transformations such as joins, aggregations, and calculated fields. This functionality enables users to integrate varied data sources into a unified dataset that can be further analyzed and visualized within the platform. Recipes provide a user-friendly interface to manipulate and merge data without the need for complex coding, making it accessible to users with varying levels of technical expertise. Reference: &type=5

To meet the requirement where each sales rep can view attainment data for reps in their division but only see their own commission data, applying a security predicate on the existing single dataset is the most efficient and secure method. This approach allows granular control over data visibility based on user-specific criteria, directly aligning with the security and privacy needs specified.

To limit visibility of the new "CardBalance" field in the Einstein Analytics dataset to only certain users, the consultant should:

• D: Add the new field to the existing dataset and use "Security Predicates" to hide it from users who should not access it. Security predicates apply row-level security to fields within datasets based on user attributes or other criteria, ensuring that sensitive data like "CardBalance" is only accessible to authorized users.

The best practice for handling user roles and permissions in Einstein Analytics is to create and assign Salesforce permission sets tailored to the distinct types of user roles. In this scenario, the consultant should recommend creating permission sets for:

• Dataset and dashboard builders (3 users),
• Dashboard creators (15 users),
• Dashboard viewers (982 users).

This approach allows for fine-grained control over access rights and ensures that each user has the appropriate level of permissions based on their responsibilities. Salesforce permission sets are a flexible and scalable way to manage access and can be tailored to the specific needs of any organization.

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.

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 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.

Before users can access Analytics in Salesforce, they need two things: an Analytics permission set license (PSL) and a permission set containing at least one Analytics user permission. The PSL enables access to the Analytics functionality at a license level, while the permission set defines what specific actions the user is authorized to perform within Analytics.

Setting security predicates for a dataset in Salesforce can be done in several ways, but not directly on each row before uploading. Security predicates are typically set in the metadata file associated with the external data file or in the transformation node of a dataflow. This is essential for managing data visibility at a granular level and ensuring compliance with organizational security policies.

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.

In Salesforce Tableau CRM, the number of concurrent queries that a user can execute is capped at 20. This limit is designed to manage the load on the analytics platform effectively, ensuring stable and responsive performance across the user base.

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: &type=5

When validating the "Why it Happened" Insights provided by Einstein Discovery, the most appropriate action for a consultant to take is to review the p-values and standard deviations associated with the insights. These statistical measures indicate the reliability of the insights and whether they are statistically significant. By examining these values, the consultant and customer can better understand the robustness and credibility of the insights.

To automate the loading of data from a Microsoft Azure SQL Data Warehouse into an Einstein Analytics dataset, transform it, and merge it with company data daily, the following Einstein Analytics user interface features can be used:

• A: Dataflow: This tool allows for the creation and management of complex data transformation and integration processes necessary for combining data sources.
• D: Recipes: Recipes provide a user-friendly interface for performing data transformation tasks without writing code, which can be used for merging and transforming data.
• E: Analytics REST API: The API can automate data ingestion and management tasks, providing flexibility and control over data integration from external systems.

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.

Einstein Discovery offers several types of insights, among which "What Happened" is considered descriptive. This type of insight focuses on describing the data, summarizing past events or outcomes without delving into causation or forecasting future trends. It's crucial for understanding the basic facts of a dataset before moving on to more complex analysis.

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

The Actions menu in a chart within Salesforce Tableau CRM (Einstein Analytics) typically includes options that enhance interactivity and integration with Salesforce features. One of the key functionalities offered by the Actions menu is the ability to post to Chatter directly from a chart. This allows users to share insights, collaborate on data visualizations, and engage with team members directly within the flow of analytics. Reference: Salesforce Help - Chatter in Dashboards

To address the issue of stages appearing in alphabetical order rather than sales cycle order in a funnel chart, a consultant can:

• Use a dataflow to rename the stages with a preceding number (A): This method involves preprocessing the data to append a numerical prefix to each stage name, thus forcing a specific order in the visualization.
• Use a dataset XMD to rename stages (D): The Extended Metadata (XMD) of a dataset allows for the customization of how data is displayed, including defining custom labels for dimensions which can enforce a particular order in the visualization.

To improve dashboard performance in the face of growing datasets, the Einstein Analytics consultant can take the following actions:

• B: Distribute steps among separate pages: This reduces the load on any single page, enhancing performance by only loading data necessary for the view being accessed.
• D: Replace separate step filters with a global filter: This reduces the number of queries and processing needed as the global filter applies universally, streamlining data handling.
• E: Move calculations to a dataflow: Offloading calculations to a dataflow minimizes the processing required at runtime, which can significantly improve dashboard performance.

The Tableau CRM Dashboard Inspector tool is designed to help optimize and troubleshoot dashboard performance by providing:

• A. Recommendations on how to improve the performance: It offers actionable suggestions to enhance dashboard efficiency.
• B. The total time required to run all queries: It aggregates the execution times of all queries involved in the dashboard, giving an overview of the dashboard’s performance.
• D. View all queries and the time it took to run each one: It allows detailed inspection of each query’s execution time, helping identify potential bottlenecks.

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.

Among the listed options, which is not a method for controlling record-level access in Salesforce:

• A: Profiles. While profiles control what a user can do broadly within the Salesforce platform (such as read, create, edit, and delete permissions on objects), they do not directly control record-level access. Record-level access is specifically managed through Sharing Rules, Organization-Wide Defaults, and Role Hierarchy.

In Salesforce Tableau CRM, with data sync enabled, the maximum number of dataflow definitions an organization can have is 25. This limit is designed to balance the need for comprehensive data operations with system performance and manageability.

When deploying models to an existing prediction in Tableau CRM, it is important to be aware of the limits imposed by the platform. Specifically, there is a limit of 10 models under a single Prediction Definition. This limit helps manage the complexity and ensures that the predictions remain manageable and efficient.

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.

The Event Monitoring Analytics app in Salesforce is specifically designed to analyze Event Log File data. This app provides insights into user behavior and system performance, helping organizations understand how their Salesforce environments are being used and how they can be optimized. The app includes prebuilt dashboards and metrics that focus on this specific type of data.

In a scenario where Recipe3 depends on Dataset1 created by Recipe1, while Recipe1 and Recipe2 have no dependencies, the optimal scheduling strategy is to make Recipe1 and Recipe2 time-based and Recipe3 event-based, triggered by the successful completion of Recipe1. This ensures that Recipe3 only processes data after Dataset1 is updated and available, maintaining data integrity and workflow efficiency.

Save changes to existing dashboards, lenses, and datasets in the app (saving dashboards requires the appropriate permission set license and permission) available in Editor and Manager*##**##*

In Einstein Analytics (Tableau CRM), the security options include:

• Inherited Security: Utilizes the existing Salesforce security model to control access.
• Row-level Security with Security Predicates: Offers granular control by restricting data visibility at the row level based on specific criteria.
• App Level Security: Manages access at the application level within Einstein Analytics.

Field-level security is not directly configurable within Einstein Analytics; instead, it's managed through the broader Salesforce security settings, affecting data visibility before it's imported into Einstein Analytics datasets. Reference: &type=5

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.

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.

The best practice to ensure Einstein Analytics dashboards are easily used across both desktop and mobile devices is to create multiple layouts and ensure the layout selectors match the device. Additionally, resizing and hiding widgets as necessary helps ensure that the content is appropriately displayed and interacted with on the device screen size.

In the context of Einstein Discovery in Tableau CRM, "Second-order analysis" refers to the type of insight that examines how one variable explains variation of the outcome variable. This analysis delves deeper into understanding the relationships between variables, particularly how they interact to influence the outcome. Second-order analysis is more advanced than first-order, which typically looks at direct relationships without considering interactions between multiple variables. This level of insight helps in identifying underlying patterns that might not be apparent from simple direct analysis. Reference: &type=5

To replace null results in the YoY Growth calculation with a 100% value when there is no account in the 'Last Year' column, the correct function to use is:

• C: coalesce(). This function is used to return the first non-null value in a list of arguments. In the formula for calculating YoY Growth, it can be used to check for a null value in the 'Last Year' data and replace it with a default value (like 100), effectively handling cases where no comparison data exists.