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Note! Einstein-Analytics-and-Discovery-Consultant has been withdrawn. The new exam code is Tableau-CRM-and-Einstein-Discovery-Consultant

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Einstein-Analytics-and-Discovery-Consultant Questions and Answers

Question # 1

Maximum concurrent API Analytics calls per org

A.

200

B.

500

C.

100

D.

50

Question # 2

A consultant is asked to configured a user to view a dashboard, within a designated app, without the ability to create and save lenses.

After adding the user to the app, which action should theconsultant take to implement this requirement?

A.

Assign the Manager role the user

B.

Ensure the user has the Analytics permission

C.

Assign the Editor role the user

D.

Assign the view role to the user

Question # 3

What do you have to assign to users before they can access Analytics?

A.

Analytics permission set license (PSL)

B.

Permission set with at least one Analytics user permission

C.

Username and password

D.

A and B

E.

B and C

Question # 4

After loading data to Einstein Discovery andcreating a story, the client asks the Einstein Consultant to explain the "Unexplained Bar" in the "Why it Happened" chart.

Which explanation is correct?

A.

It should always be 0 or the model should not be used.

B.

It shows the difference between the predicted outcome and the observed outcome.

C.

It displays the difference between the analyzed data and the data not reviewed.

D.

It only appears for outcomes that do not have an explanation.

Question # 5

A dataset for building the Einstein Discovery story contains 72 fields that are potentially relevant predictors.

Which approach is considered best practice to assess the top predictors in order to get to a meaningful and robust model?

A.

This dataset is too big and cannot be used in Einstein Discovery. Request a new dataset with fewer predictors.

B.

Build the story with all the predictors and indicate that Einstein Discovery should show the top predictors.

C.

Go back to the data preparation and reduce the number of fields to less than 30 in order toproduce a story.

D.

Build a story with a first set of predictors and assess which predictors are important to the story. Then drop the less important ones and add the predictors that were omitted in the first run and assess their impact.

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