Tableau-CRM-and-Einstein-Discovery-Consultant Practice Exam Questions with Answers Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Certification

Creating a regression timeseries in Salesforce Tableau CRM involves the use of the SAQL (Salesforce Analytics Query Language). Specifically, the timeseries function is used within SAQL to model and forecast data points across time. Here are the required steps:

• Partition: This argument in the timeseries function specifies how the data should be grouped. In most cases, this will be based on a time dimension such as date or month.
• Seasonality: Setting this parameter to 12 is typical for capturing yearly patterns and trends in the data.

In Salesforce Tableau CRM Einstein Discovery:

• B is correct because in the Story Settings, the consultant can view and evaluate the strength of each predictor field, helping to determine which fields are most influential in the model.

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

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.

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.

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

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

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 REST API URL where a consultant can find current template apps in Salesforce Tableau CRM (formerly Einstein Analytics) is "/services/data/vXX.X/wave/templates". This endpoint is specifically designed for interacting with template apps, allowing consultants to extend, customize, or retrieve information about available templates.

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.

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.

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 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.

A merge key in a dataset recipe is used to join related data from different sources within Tableau CRM. By specifying merge keys, users define the common fields between datasets that serve as the basis for combining records. This process is critical for creating comprehensive datasets that pull in relevant data from disparate sources, ensuring data accuracy and consistency across the joined datasets. The use of merge keys facilitates the alignment of data based on shared attributes, crucial for accurate analysis and reporting. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_prep_recipes_join_data.htm &type=5

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.

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.

Upon receiving a data alert from Einstein Discovery suggesting the presence of outliers in a dataset used to maximize daily sales quantity, appropriate actions include: B) Removing the outliers as suggested and then verifying if the removal has led to an improvement in model metrics and story insights. This ensures that the model's predictive accuracy is not adversely affected by extreme values. C) Engaging the client in a discussion about the occurrence of extreme values (both low and high) to determine if such cases are anomalies that should be excluded from analysis, ensuring that the model reflects typical sales scenarios.

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 with different 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.

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

To add text, numerical values via a case statement, and dates via SAQL in an existing dataflow, the correct compute option is "computeExpression." This function allows for the creation and manipulation of new fields based on expressions applied to existing data in the dataflow, suitable for the required transformations.

In Tableau CRM, the default number of rows displayed in a compare table is 1,000. This setting provides a balance between detailed data exploration and system performance, ensuring users can analyze significant amounts of data without overwhelming the interface or processing capabilities.

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 Salesforce Analytics Query Language (SAQL), a keyword refers to specific 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 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

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

The correct timeseries statement to fix the SAQL query for predicting sales in each sales region is:

• C: q = timeseries q generate 'sum_Sales' as 'Forecasted_Sales' with (dateCols=('Date_Year', 'Date_Quarter', "Y-Q"), partition='Region', seasonality=4). This statement correctly specifies the required parameters for a timeseries analysis, including the date columns, the partition by 'Region', and the seasonality factor, which is crucial for accurate forecasting in time series 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

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*##**##*

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.

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.

Faceting in Tableau CRM (formerly Einstein Analytics) is a powerful feature that allows different charts and components within a dashboard to interact with each other. When you make a selection in one chart, it automatically applies that filter across other charts within the same dashboard. This interconnected filtering enhances the analysis by allowing users to explore data across multiple dimensions simultaneously, providing a more holistic view of the data set. Faceting ensures that all visual components are contextually relevant, improving the user's ability to draw insights from the data. Reference: https://help.salesforce.com/s/articleView?id=sf.bi_dashboards_faceting.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 optimizations and step adjustments. C. View all queries and the time it took to run each one. This feature helps in identifying slow-running queries that may need 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