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

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 textsor 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 Einstein Analytics Dashboard Inspector is a tool used to analyze and improve dashboard performance. It allows users to: B. Get a list of recommendations on how to improve the performance of the dashboard. This includes suggestions on query optimizationsand step adjustments. C. View all queries and the time it took to run each one. This feature helps in identifying slow-running queries that mayneed optimization. D. View the total time required to run all queries. This gives an overall view of the dashboard's performance, which is crucial for troubleshooting and performance enhancement. These tools help in maintaining optimal dashboard performance and user experience. Reference: Salesforce Documentation on Dashboard Inspector

In Einstein Analytics, the "sfdcDigest" node is used in a dataflow to extract data from a registered dataset. This node is specificallydesigned to query and ingest data from Salesforce objects and datasets into the dataflow for further transformations and analyses.

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 thereliability 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 address the requirement where some users need to create their own dashboards and others only need to use them, the best practice is to:

A: Create two permission sets withdifferent system permissions. One permission set would grant capabilities necessary for creating and managing dashboards, likely including permissions like "Create Dashboard," "Edit Dashboard," etc. The other permission set would grant only "View" permissions, restricting users to dashboard usage without modification capabilities.

The strength of the model depicted in the chart cannot be determined conclusively from the metrics shown (GINI coefficient of 0.535, maximum accuracy of 0.7597, and threshold of 0.5163). These metrics indicate aspects of the model's performance,but they do not provide a complete picture of its effectiveness in real-world scenarios. For instance, the GINI coefficient suggests some degree of discriminative power, yet it is not high enough alone to declare the model very effective. Similarly, the maximum accuracy shows potential, but without context regarding the complexity of the prediction task or comparisons to baseline models, a definitive conclusion about the model's strength cannot be drawn.

In Einstein Discovery stories, insights showhow 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

Based on the "Why it Happened" graphic: A. Germany performs lower than average but better when the product is Call Center. This interpretation is supported by the presence of the Call Center product in Germany as a factor that increases the probability of Opportunity Won, contrary to its general performance. D. Germany sells more Call Center product and that helps them increase their win rate. The graph indicates that Call Center products have a positive impact in Germany on the win rate, suggesting that the higher sales volume of this product helps improve overall performance in the region.These insights are derived from understanding the visualization in the context of what contributes positively and negatively to theoutcomes, which is a key feature of Einstein Analytics. Reference: Einstein Discovery Insights

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.

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 usingsecurity 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.

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 inthe dataset need to reflect consistently, including computed or derived fields.

In Salesforce Analytics Query Language (SAQL), a keyword refers tospecific commands or functions such as 'group', 'foreach', 'filter', and more. These keywords are essential for structuring queries and must be used in lowercase to be recognized by the system. Keywords control operations on data streams within the queries, facilitating complex data manipulations and analyses.

In Salesforce Analytics Query Language (SAQL), the order of functions plays a crucial role in how data is processed. The 'filter'and 'order' functions can be used interchangeably depending on the specific needs of the query. However, 'offset' should always follow either 'filter' or 'order', and 'limit' must be specified after 'offset'. This order ensures that the query first narrowsdown the data set, then offsets into this subset, and finally limits the number of records returned.

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 totraditional 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.

* 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.jsoncontains 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. Oryou 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, ifthe wizard asks which fields to include in filters for accounts, template-to-app-rules.json determines how that choice is reflected in dashboards.

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 thedata 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 create a results binding in Salesforce Einstein Analytics that enables a table from one dataset to be filtered based on the results of a chart from anotherdataset, follow these steps:

A. Look up the API name of the filtering field: This is necessary to understand and reference the specific field in the dataset that you want to use for filtering.

B. Look up the API name of the source field: You need to identify the field in the source dataset (the one used by the chart) that will provide the filtering criteria.

C. Find source and target step names: Identifying the source (chart) and target (table) step names in the dashboard is crucial for configuring the binding correctly. These names are used in the binding syntax to define where the source data is coming from and where it should apply.

To properlyanalyze the Opportunity win rate using Einstein Discovery, it's crucial to:

D: Change the grain by removing the line level detail as it will skew the win rate. This modification ensures that the analysis focuses on the opportunities as a whole rather thanthe potentially misleading details of individual line items, providing a more accurate view of the overall win rate.

From the description of the tooltip and the insight provided:

A is correct because it specifically compares the average daily sales in San Francisco to a global average, showing asignificant difference.

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.

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 reportsoutside of Salesforce.

Data that has already been loaded into EinsteinAnalytics as datasets can be utilized directly in Einstein Discovery without requiring a separate load process. This integration within the Salesforce platform allows seamless transition of data from analytics to discovery, facilitating a streamlined workflow for data analysis and enhanced decision-making. Therefore, Danielle can use existing datasets in Einstein Analytics for her analysis in Einstein Discovery without needing additional permissions or import steps. Reference: Salesforce Help - Using Data in Einstein Discovery

To enhance the performance of data synchronization in Tableau CRM, after removing unused objects and fields, the next logical step is to evaluate the connection mode for each connected object. Thisinvolves analyzing whether the current connection settings (e.g., full or incremental sync) are optimized based on the data volume and update frequency. Adjusting these settings can significantly improve sync performance by reducing unnecessary data transfer and processing.

In Tableau CRM, each run of a recipe counts towards the 24-hour rollingrecipe 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.

For Discovery in Salesforce Tableau CRM, the limits on the number of columns (variables) are setat a minimum of 2 and a maximum of 50. These boundaries ensure that the Discovery models are both manageable and robust enough to generate meaningful insights without overwhelming the computational resources.

In Salesforce Analytics, the basic actions within a query that are fundamental to building meaningful visualizations and data summaries include:

A. Aggregate by a measure: This involves summarizing data using statistical measures like sum,average, maximum, etc., which help in understanding the distribution and trends of data.

C. Filter to narrow down your results: Filtering allows users to limit the data shown in the dashboard to only those records that meet certain criteria, thus focusingon more relevant subsets of data.

D. Grouping by a dimension: Grouping involves categorizing data based on specific fields (dimensions), which helps in analyzing the data across different segments or categories.

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.

In Salesforce Tableau CRM (Einstein Discovery), one of the straightforward ways to get data into Einstein Discovery for analysis is by using an Einstein Analytics dataset. These datasets can be directly used to create stories in Einstein Discovery, allowing for deep analysis and predictive modeling based on thedata already collected and structured within the Salesforce environment. This method leverages the integrated nature of Salesforce products to provide a cohesive and efficient analyticsexperience. Reference: Salesforce Help - Data Preparation for Einstein Discovery

https://trailhead.salesforce.com/content/learn/modules/wave_exploration_smart_data_discovery_basics/wave_get_smart_data_discovery_and_data

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 serviceanalytics, 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

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.

Before releasingan 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 asadding or removing charts.

Posting to Chatter from a Salesforce Wave (Tableau CRM)dashboard is done by selecting "Post to Chatter" from the Actions drop-down menu at the top of the dashboard. This feature allows users to directly communicate findings, share insights, or discuss data visible on the dashboard with other team members through Salesforce Chatter, enhancing collaboration and information sharing. Reference: Salesforce Documentation - Post to Chatter from Dashboards

To increase adoption among account managers who need comprehensive account overviews, embedding a dashboard onto the account record page with filters that dynamically adjust to show relevant data about active cases, opportunities, and product whitespace is most effective. Here’s how to implement this:

Design a comprehensive dashboard in Tableau CRM that includes all the necessary lenses and widgets to show key details such as active cases,opportunities, and potential product sales opportunities.

Ensure the dashboard can dynamically filter data based on the account being viewed. This typically involves using dynamic dashboard filters linked to the account record page.

Embed the dashboard onto the Salesforce account record page. This integration allows account managers to have all the necessary information directly within the context of the account they are viewing, enhancing usability and relevance.

This approach promotes better user engagement by providing relevant data directly within the user's workflow.

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 ofthe 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 add a new field, SalesTax, to a Product Sales dataset and calculate its value using the formula (SubTotal + CountyTax), the correct node to use in a recipe is the Update node. This node allows you to modify existing records by addingnew fields or updating values in existing fields based on defined calculations.

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

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.

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.

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 thesuccessful completion of Recipe1. This ensures that Recipe3 only processes data after Dataset1 is updated and available, maintaining data integrity and workflow efficiency.

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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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.

Insights categorized as 'Unrelated' in Einstein Discovery are significant because they highlight factors or variables that, while not directly related to the primary variables of interest, may still influence the outcome through broader or global effects. These insights help in understanding the comprehensive landscape of the data, acknowledging that some influencing factors might be external or less obvious yet impactful. This broader view aids in a more holistic analysis and prevents oversight of potential influences outside the immediate dataset.Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_insights_unrelated.htm &type=5

In Tableau CRM (formerly Einstein Analytics), you can restrict access to data at the row level using security predicates. Security predicates are filter expressions added to datasets that dynamically restrict data access based on user attributes or record specifics. This method allows administrators to enforce data security policies directly within the dataset, ensuring that users only see data relevant and permitted to them, without altering the underlying data structure like removing rows manually or adding flags. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security_predicates.htm &type=5

Predicate is a name for a filter condition that defines row-level access to records in a dataset Example: "rowLevelSecurityFilter":"'AccountOwner' == \"$User.Name\""

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 allow users who belong to an account teamto 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.