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Free Certified Einstein Analytics and Discovery Consultant Exam Braindumps
Certified Einstein Analytics and Discovery Consultant Questions 46–50 (page: 11)

Updated On: 10-Feb-2026
Page 11 of 43
PDF with 209 Questions

What are Einstein Analytics prebuilt permission sets? Select 2:

  1. Einstein Analytics Platform Admin
  2. Einstein Analytics Platform User
  3. Einstein Analytics
  4. Einstein Analytics Superadmin

Answer(s): A,B



After getting approval of the dashboard layout design for a desktop, the Einstein Analytics consultant is ready to start the design process for a mobile layout.
What are three considerations that the consultant should keep in mind when developing the layout? Choose 3 answers

  1. If no layouts are eligible for the mobile device, the first defined layout is used. (Missed)
  2. If no layouts are eligible for the mobile device, an error message will be displayed.
  3. If more than one layout is eligible, the one with the most device properties set is used. If there is a tie, the most recently defined layout is used. (Missed)
  4. A layout for mobile is eligible for use when the device meets all the device properties set in the Layout panel.
  5. There are widgets that cannot be displayed on mobile layouts.

Answer(s): A,C,D

Explanation:

https://trailhead.salesforce.com/en/content/learn/modules/wave_dashboard_designer_basics/wave_take_analytics_on_the_go



A consultant wants to optimize data loads by extracting Salesforce objects using independent Einstein Analytics dataflows ahead of time. Which construct should be used to accomplish this?

  1. Augment
  2. Dataflow
  3. Clone
  4. Data Sync (Replication)

Answer(s): D



When Analytics runs a dataflow that accesses Salesforce objects, which user does it use?

  1. The current user
  2. The system administrator
  3. The Integration User
  4. The record owner

Answer(s): C



In a story investigating margin, large variations between product groups are dominating the story. Which action might an Einstein Consultant take to resolve this?

  1. Add more data to the dataset.
  2. Normalize the margin by product group.
  3. Eliminate extreme values in the dataset.
  4. Run separate stories for each region.

Answer(s): C



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