Salesforce braindumps2go tableau-crm-and-einstein-discovery-consultant Leak Questions by Arnold q84 vce pdf

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Exam Name:	Salesforce Tableau CRM Einstein Discovery Consultant(SP23)
Exam Code:	Tableau-CRM-and-Einstein-Discovery-Consultant Dumps
Vendor:	Salesforce	Certification:	Salesforce Consultant
Questions:	242 Q&A's	Shared By:	arnold

Question 8

When creating a story in Einstein Discovery, do all potential collinear fields need to be removed before executing the build story'5

Options:

No. Einstein Discovery is impervious to collinearity, so the story and subsequent model will be fine.

No. Although it is ideal to eliminate collinearity as soon as possible, Einstein will give a warning post-build and the ridge regression will prevent collinearity from over-fitting.

yes. If all collinear variables are not excluded, the model will over-fit and not make any sense.

Yes. If the collinear variables are not removed, the Einstein Discovery model build will fail.

Discussion

Question 9

Creating an Einstein Discovery story involves:

Options:

Selecting a Dataset in Einstein Analytics, then clicking Create Story

Copying text from company reports and pasting into Einstein Discovery

Uploading a Microsoft® PowerPoint® presentation

Entering notes about the data

Discussion

Question 10

What kind of org should you use for checking challenges when you do Trailhead modules about Analytics?

Options:

An enterprise org, because that type of org is typically used by large companies

A trial org, because you don't need to save anything

A Developer Edition org, because it's a free, safe environment where you can try things out

A higher education org, because analytics requires advanced math

Discussion

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Question 11

A consultant built an Einstein Analytics dashboard for a company. The company then requested an enhancement to the dashboard and provided additional data that needs to be displayed. As a result, the consultant decides to "augment" the dataflow.

Which phrase describes this transformation?

Options:

Add data based on the right-side grain

Join data similar to an SQL statement

Add data as a lookup relationship (left side is the lowest grain)

Join data based on a many-to-many relationship