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

Page: 12 / 17

Question 48

Which statement best describes how to ensure Einstein Analytics dashboards are easily used across both desktop and mobile devices?

Options:

Create multiple layouts, and reorder all the widgets so that they fit nicely within the new default width.

Create a single layout and allow Einstein Analytics to automatically organize dashboard contents to be optimal for the device type.

Create a single layout and reorder all the widgets so that they fit nicely when viewing on either device.

Create multiple layouts, ensure the layout selectors match the device, and resize/hide widgets as necessary to ensure the content is appropriate for the device screen size.

Question 49

A large company is rolling out Einstein Analytics to their field sales. They have a well-defined role hierarchy where everyone is assigned to an appropriate node on the hierarchy.

An individual Sales rep should be able to view all opportunities that she/he owns or as part of the account team or opportunity team. The Sales Manager should be able to view all opportunities for the entire Sales team. Similarly, the Sales Vice President should be able to view opportunities for everyone who rolls up in that hierarchy.

The opportunity dataset has a field called 'Ownerld' which represents the opportunity owner.

Given this information, how can an Einstein Consultant implement the above requirements?

Options:

As part of the dataflow, use the flatten operation on the role hierarchy and create a multivalue attribute called 'ParentRolelDs' on the

opportunity dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" && 'Ownerld' == "SUser.Id".

As part of the dataflow, use computeExpression on the Roleld field to create an attribute called 'ParentRolelDs' on the opportunity

dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || 'Ownerld' == "$User.Id".

As part of the dataflow, use computeRelative on the Roleld field to create an attribute called 'ParentRolelDs' on the opportunity

dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || 'Ownerld' == "$User.Id".

As part of the dataflow, use the flatten operation on the role hierarchy and create a multivalue attribute called 'ParentRolelDs' on the

opportunity dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || TeamMember.Id' == "$User. Id" || 'Ownerld' == "SUser.Id".

Question 50

A consultant is working with a credit card company that needs help with ongoing fraudulent transactions. The company provides a representative sample dataset for the consultant to analyze in Einstein Discovery. The story's initial assessment shows that a third-party payment app is the source of these fraudulent transactions. However, the company rejects this assessment outcome, stating they have not had a partnership with this payment app long enough for it to be a concern.

What is the recommended next step to improve the story outcome?

Options:

Make adjustments to the story to better demonstrate that the third-party payment app is the culprit.

Use the credit card company's domain knowledge and exclude the third-party payment app from the story.

Explain to the company that the story has returned unbiased results and the initial assessment is accurate.

Ask the credit card company for a more comprehensive dataset to analyze.

Question 51

In Einstein Discovery:

Options:

'What Is The Difference' insights are comparative insights that help you better understand the relationships between explanatory variables and the goal (target outcome variable) in your story. These insights, based on a statistical analysis of your dataset, help you figure out which factors contribute to the biggest changes in the outcome variable. Einstein Discovery uses waterfall charts to help you visualize comparisons in What Is The Dif

'Why It Happened' insights help you take a deeper look into the exact factors that led to an outcome. Why It Happened s/ Q insights drill deeper into the various factors that contributed to your story's goal. These insights are based on a statistical analysis of your dataset. Einstein Discovery uses waterfall charts to help you visualize Why It Happened insights.

'Predictions and Improvements' insights help you explore what might happen in the future. For example, you can interactively perform "what if analyses in your story. Einstein Discovery provides you with predictions and suggested improvements based on a statistical analysis of your dataset and predictive analytics. To help you visualize these insights, Einstein Discovery uses:

- waterfall charts for predictions

- bar charts for s

'What Happened' insights are the primary insights in your story. They are descriptive insights that help you explore, at an y/ Q overview level, what factors contributed to the outcome, based on a statistical analysis of your dataset. .Einstein Discovery uses bar charts to help you visualize What Happened insights.