Salesforce Certified CRM Analytics and Einstein Discovery Consultant CRM Analytics and Einstein Discovery Consultant Dumps in PDF

Free Salesforce CRM Analytics and Einstein Discovery Consultant Real Questions (page: 43)

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?

  1. 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' == "$User.UserRoleId" && 'Ownerld' == "SUser.Id".
  2. As part of the dataflow, use computeExpression on the Roleld field to create an attribute called 'ParentRolelDs' on the opportunity dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || 'Ownerld' == "$User.Id".
  3. As part of the dataflow, use computeRelative on the Roleld field to create an attribute called 'ParentRolelDs' on the opportunity dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || 'Ownerld' == "$User.Id".
  4. As part of the dataflow, use the flatten operation on the role hierarchy and create a multivalue attribute called 'ParentRolelDs' on the opportunity dataset and apply following security predicate: 'ParentRolelDs' == "$User.UserRoleId" || TeamMember.Id' == "$User. Id" || 'Ownerld' == "SUser.Id".

Answer(s): D



The Universal Containers company plans to upload target data from an external tool to Einstein Analytics so they can calculate the Sales team target attainments.

The target data changes every month, so the datasets need to be updated on a monthly basis. The target data is a CSV file that contains the Salesforce ID of the sales representative, the target amount, and the month of the target. For each sales representative, the file contains a target for every month of the current year as well as all previous years.

Based on this information, which operation should a consultant use with the Analytics External Data API to upload the file?

  1. Update
  2. Append
  3. Overwrite
  4. Upsert

Answer(s): C



What are two benefits of designing using the "Progressive Disclosure" principle? Choose 2 answers

  1. improved ease of use for end users
  2. Discounted EA licenses when growth is achieved
  3. Automatic conditional formatting
  4. Better dashboard performance

Answer(s): A,D

Explanation:

https://developer.salesforce.com/blogs/developer-relations/2017/04/lightning-components- performance-best-practices.html



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?

  1. This dataset is too big and cannot be used in Einstein Discovery. Request a new dataset with fewer predictors.
  2. Build the story with all the predictors and indicate that Einstein Discovery should show the top predictors.
  3. Go back to the data preparation and reduce the number of fields to less than 30 in order to produce a story.
  4. 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.

Answer(s): D

Explanation:

https://medium.com/@kshannon565/ea-certification-study-guide-part-3-einstein-discovery-story- design-70ffbe4666c2



A list widget is added to a dashboard with existing charts and tables.
What must be true for the list widget to facet the dashboard charts and tables using widget properties?

  1. The list, chart, and table steps must share a common name.
  2. The list, chart, and table steps must share common dimensions from different datasets.
  3. The list, chart, and table steps must share the same dataset.
  4. Chart and table steps must have their own list widgets.

Answer(s): C



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