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

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.

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.

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.

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.

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.

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.

To optimize data loads by extracting Salesforce objects ahead of time, the correct construct to use is "Data Sync (Replication)." This feature in Salesforce Einstein Analytics allows for the periodic refresh of datasets from Salesforce objects without needing to rerun the entire dataflow. This optimization helps in managing large datasets more efficiently by keeping the data up-to-date with less processing time and system load.

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.

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.

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.

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 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.

The Chi-square test in statistics is used to identify whether there is a significant association between categorical variables. It is especially useful in scenarios like in Tableau CRM where one might want to determine if the differences between observed and expected frequencies in one or more categories are just random variations or are statistically significant. This test helps in identifying categories that exhibit a pattern that deviates from the overall average, making it a valuable tool for data analysis in categorical data sets. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_edd_statistical_tests.htm &type=5

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 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 scenario described where Yasmine cannot see any rows in the Seed Bank Orders dataset while Hadiqa can, likely indicates that the dataset's security predicate was set up to restrict data visibility based on user roles or attributes. Security predicates in Salesforce Tableau CRM are used to implement row-level security, ensuring that users only see data pertinent to their role or permissions within the organization. This security setup is crucial for maintaining data privacy and compliance. Reference: Salesforce Help - Row-Level Security

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

To enable Analytics for Communities in Tableau CRM, you need specific prerequisites:

• License Requirements: A Customer Community Plus or Partner Community license is necessary, enabling more extensive CRM functionalities for community members.
• Permission Set License: Community members must be assigned the Analytics for Communities permission set license. This setup ensures that community users have the appropriate access to use and benefit from Analytics features within their community environments.

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: https://help.salesforce.com/s/articleView?id=sf.bi_edd_understanding_analysis.htm &type=5

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 Salesforce Tableau CRM, the timeout for ETL (Extract, Transform, Load) jobs that have been scheduled but not executed is 15 minutes. This timeout setting helps manage system resources efficiently by ensuring that jobs do not indefinitely occupy system capacity if they fail to initiate.

To create a lens in Einstein Analytics that shows the total budget for each region for each month, including combinations with no data:

• A: Use a SAQL query leveraging the "fill" statement with a "partition" parameter. The "fill" function in SAQL can be used to ensure that every combination of region and month is displayed, filling in zeros where no data exists. This is essential for complete and accurate budget tracking across all regions and months.

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 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.

The most straightforward and effective way to ensure that temperature data is easy to interpret on Einstein Analytics dashboards is to add a text widget to the dashboard indicating whether the temperature is in Fahrenheit or Celsius. This method provides clear, unambiguous context directly within the dashboard view, improving user understanding at a glance.

When discrepancies arise between the aggregate sum in a dataset and manual calculations in Salesforce, common issues include: C. Differences involving formula fields. Formula fields in Salesforce are calculated dynamically and may not always be calculated during data sync if they depend on other fields that aren't triggered by record updates. D. Differences involving trigger-updated fields. Fields that are updated by triggers might not have their latest values captured if the triggers were executed after the data was replicated. These scenarios can cause inconsistencies in data reflected in Einstein Analytics compared to what is stored directly in Salesforce. Reference: Salesforce Documentation on Data Sync and Replication

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.

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 another dataset, 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.

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.

When you select one variable in an Einstein Discovery story and examine its insights, you are comparing the impact of that selected variable with all other data in the story. This involves analyzing how much the selected variable influences the outcome variable relative to other variables in the dataset. The process evaluates the contribution of the selected variable to the overall variance in the outcome, offering insights into its significance and relationship within the broader data context. Reference: Salesforce Einstein Discovery Story Insights

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.

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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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

When migrating an Einstein Analytics dashboard from a sandbox to production, several options are available:

• Ant Migration Tool (A): This tool can be used for deploying metadata (including dashboards) between Salesforce environments.
• Analytics REST API (C): This API can programmatically handle the deployment of dashboards from one environment to another.
• Change sets (D): Change sets can include Einstein Analytics assets such as dashboards for migration between connected Salesforce environments.

Before users can access Tableau CRM (Einstein Analytics), they must be assigned specific permissions. These include: A. An Analytics permission set license (PSL), which grants the user the license to use Analytics. B. A permission set configured with Analytics user permissions, which detail what aspects of Analytics the user can access. This setup ensures that both the licensing and access rights are correctly aligned to user roles and needs within the organization. It is essential to configure both aspects to enable functional and compliant use of the platform. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_security.htm &type=5

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.

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.

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\""

Outliers in a dataset can significantly impact the results of a machine learning model, including those built by Einstein Discovery.expand_more Here's why changing binning methods is often the best approach:

• Outliers and Machine Learning: Outliers are extreme values that fall far outside the typical range of data.expand_more They can distort statistical summaries and skew the model's understanding of relationships within the data.
• Binning: Binning is the process of dividing continuous numerical data into discrete groups or bins.expand_more In Einstein Discovery, binning affects how data is visualized and analyzed.
• Fixed-width Binning: With fixed-width binning, you define equal-sized intervals for grouping data points. This can help mitigate the impact of outliers by spreading their influence across a wider range, reducing their effect on individual bins and the overall model.

Why other options are less ideal:

• A. Remove the outlier rows: While removing outliers can be effective, it risks discarding potentially valuable information. It's best used only when outliers are definitively incorrect.
• B. Nothing, because the field is not the outcome variable: Even predictor variables (inputs) with outliers can negatively impact the model. Outliers distort relationships the model is trying to learn.expand_more
• D. Remove the field because it has bad data: This is too drastic unless the whole field is unreliable. Binning adjustments are often enough to address outlier issues.

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, 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.

To delete activities in Dataset A related to users in Dataset B, the consultant can use a combination of dataflow transformations: "augment" and "filter." First, augment Dataset A with Dataset B using "userID" to combine the datasets based on user association. Then, apply a filter to retain only those records in Dataset A that do not have a corresponding entry in Dataset B.

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 narrows down the data set, then offsets into this subset, and finally limits the number of records returned.