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Salesforce Tableau-CRM-Einstein-Discovery-Consultant Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Einstein Discovery | - Predictive Modeling Concepts
|
| Analytics Dashboarding | - Lenses and Dashboards
|
| Security and Access | - Row-Level Security
|
| Data Layer and Data Management | - Data Modeling and Data Sources
|
| Data Preparation and Dataflows | - Data Preparation Tools
|
Salesforce Tableau CRM Einstein Discovery Consultant Sample Questions:
Question 1
What kind of insight is "What Is The Difference"?
A. Predictive
B. Descriptive
C. Selective
D. Diagnostic
E. Prescriptive
Question 2
You are asked to update and maintain your company's Einstein Analytics dashboards.
A request comes in for one of the dashboards that contains steps from different datasets. The request is to make it possible for a table from one dataset to be filtered by the results of a chart from another dataset. Your solution is to create a results binding.
Which three steps should you implement to create the binding?
A. Look up the API name of the filtering field
B. Configure the results binding on the target step in the dashboard JSON.
C. Look up the API name of the source field
D. Find source and target step names
Question 3
Why is it important to select the right root object when you use the dataset builder?
A. The root object determines what related objects you can add to the dataset.
B. The root object determines who can see the dataset.
C. The root object determines what app the dataset is saved in.
D. Only data from the root object can be included in the dataset.
Question 4
You can configure access to data at all of the following levels, except:
A. Organization
B. Page Layouts
C. Records
D. Objects
Question 5
A consultant has created a story to maximize the daily sales quantity of consumer products in stores. After creating a story, the consulting is presented with this data alert by Einstein Discovery (see graphic).
What are two appropriate actions to take?
Choose 2 answers
A. Manually remove the sales bellow 0 (negative sales must be a data issue), but keep the large value (the more data, the better the model will be).
B. Remove the outliers as suggested by Einstein and deploy the model.
C. Discuss with the client if values below 0 and above 2,489 are so uncommon that they should perhaps be left out of the story.
D. Remove the outliers as suggested by Einstein, and verify using model metrics and story insights if the quality improved.
Solutions:
| Question 1 Answer: D | Question 2 Answer: A,B,D | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: B,C |
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