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

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.

To meet the requirement where each sales rep can view attainment data for reps in their division but only see their own commission data, applying a security predicate on the existing single dataset is the most efficient and secure method. This approach allows granular control over data visibility based on user-specific criteria, directly aligning with the security and privacy needs specified.

Predictive insights are primarilybeneficial for:

C: Drilling down into the underlying reasons behind a prediction. This capability enables users to understand not just what might happen in the future but why those outcomes are likely, based on the data being analyzed. This understanding is crucial for strategic decision-making and for optimizing processes based on predictive analytics.

The correct set of statements that generate a monthly amount on a cumulative basis annually is Option B. This option likely includes cumulative sum functions applied within a grouped statement, typically done with a grouping by 'Month'and an annual reset. Such SQL-like syntax is commonly used in Tableau CRM for time-based cumulative calculations.

In the configuration wizard ofTableau CRM apps, required fields or questions without default values are typically indicated by the system to guide the setup process. By proceeding with the configuration ("Looks good, next"), the wizard will highlight any mandatory questions that havenot been answered, usually by marking them in red. This visual cue allows users to easily identify and address the required inputs that are missing defaults, ensuring that essential configurations are not overlooked during setup.Reference: https://help.sa lesforce.com/s/articleView?id=sf.bi_apps_configure.htm&type=5

Upon receiving a data alert from Einstein Discovery suggesting the presence of outliers in a dataset used to maximize dailysales 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 affectedby 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.

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.

XMD standsfor Extended Metadata. This component in Salesforce Tableau CRM allows users to customize datasets and dashboards further. With XMD, users can define label overrides, specify numeric formats, set up color coding, and more, enhancing the visualization andinterpretation of data within the analytics environment.

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 dynamicallyand 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 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.