Free Practice Questions for Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant Exam

The widget property that allows a consultant to restrict the view to a domain bounded by the values entered is "Add Custom Range." This property enables setting specificrange limits on the data displayed in the widget, effectively controlling the scope of data visualization to ensure focus on relevant metrics.

To replace null results in the YoY Growth calculation with a 100% value when there is no account in the 'Last Year' column, the correct function to use is:

C: coalesce(). This function is used to return the first non-null value in a list of arguments. In the formula for calculating YoY Growth, it can be used to check for a null value in the 'Last Year' data and replace it with a default value (like 100), effectively handling cases where no comparison data exists.

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 verifyif 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 datasetsneeded for the dashboards.

The Service Analytics Overview dashboard in Tableau CRM is designed to provide comprehensive insights into various aspects of service operations. It includes:

Key metrics such as open cases, average time to close, first contact resolution, and customer satisfaction, offering a quick overview of service efficiency.

Drill-down capabilities into more detailed dashboards like agent performance, channel review, and telephony metrics, enabling deeper analysis.

Availability on both desktop and mobile platforms, ensuring accessibility across different devices.

It serves as an excellent starting point for serviceanalytics, providing a holistic view of the service department’s performance.

This dashboard is a central tool for managing and analyzing service operations effectively, helping organizations to optimize their customer service strategies.Reference: https://help.salesforce.com/s/articleView?id=sf.bi_service_analytics.htm&type=5

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, the limit for uploading external files to a dataset on a rolling24-hour basis is 50 files. This limitation is designed to manage the data ingestion process effectively and ensure optimal performance of the analytics system.

To limit visibility of the new "CardBalance" field in the Einstein Analytics dataset to only certain users, the consultant should:

D: Add thenew field to the existing dataset and use "Security Predicates" to hide it from users who should not access it. Security predicates apply row-level security to fields within datasets based on user attributes or other criteria, ensuring that sensitive datalike "CardBalance" is only accessible to authorized users.

The best connection mode for Universal Containers, given their need to sync updated and new records from the Order object weekly, including updates to formula fields, is Periodic Full Sync. This mode will:

Periodically sync all records from the source object (in this case, weekly).

Ensure that all fields, including formula fields that may calculate values based on other periodically updated fields, are current in the destination system.

This mode is especially suitable when data completeness and accuracy are crucial, and where changes inthe dataset need to reflect consistently, including computed or derived fields.