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

A Bar chart is best suited to track the progress of sales revenue that is realized versus the target. Bar charts are effective for comparing multiple categories of data, such as actual versus target sales, side by side. This visual comparison makes it clear to see how close the realized sales are to the targets, which is crucial for quick assessment and decision-making in sales performance monitoring.

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.

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.

To address the performance issue with data sync (replication) caused by unnecessary data being synced, the consultant should:

• D: Update the existing data sync (replication) process by removing unused fields with a filter to extract relevant data. This approach minimizes the data volume being synchronized by focusing only on relevant fields and records, thereby improving the performance of the data sync processes.

Einstein Discovery in Salesforce functions like having a personal data scientist on staff. It utilizes advanced machine learning algorithms to analyze large volumes of data, identify patterns, and provide predictive insights and recommendations. This tool simplifies complex data analysis tasks and enables users to make informed decisions without the need for deep statistical knowledge or expertise in data science. Reference: Salesforce Einstein Discovery Overview

The appropriate action when Einstein Discovery surfaces an unexpected pattern is to determine if the pattern is a data issue or a new insight. This approach involves verifying the integrity and accuracy of the data, and understanding whether the pattern represents a meaningful insight into the business operations or an anomaly caused by data issues.

To convert the "CloseDate_Month" numerical field into a month name in the dataset, the consultant should use:

• C: computeExpression. This Dataflow node allows the transformation of existing data fields using expressions, such as converting a month number into a month name. By applying a suitable expression, the 'CloseDate_Month' can be transformed into a string representing the month’s name, enhancing readability and usability in filters.