Tableau-CRM-and-Einstein-Discovery-Consultant Salesforce Tableau CRM Einstein Discovery Consultant (SP25) Free Practice Exam Questions (2025 Updated)

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

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.

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.

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.

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

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 deeperanalysis 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

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

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, itautomatically 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

To resolve theissue where selections in the List widget do not affect the Pie chart due to the use of different datasets in an Einstein Analytics dashboard, an Einstein Consultant can:

A: Use 'Connect Data Sources' and create a connection to connect the two datasets. This approach establishes a relationship between the datasets, enabling interactions between widgets using these datasets to affect each other.

B: Use selection binding in the filters section of the step "Step_pie_2." Selection binding will allow the Pie chart to dynamically adjust based on selections made in the List widget, by applying the selected filters directly to the Pie chart's dataset.

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.

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 Salesforce Tableau CRM, the limit on the number of queries that a user can execute per day is set to 50,000. This is designed to ensure optimal performance across the platform while providing sufficient capacity for users tocarry out extensive data analysis throughout the day.