Tableau-CRM-and-Einstein-Discovery-Consultant Exam Dumps - Salesforce Consultant Questions and Answers

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

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.

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 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 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.

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 to traditional 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.

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 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

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