Tableau-CRM-and-Einstein-Discovery-Consultant Practice Questions

A.  

Selecting a Dataset in Einstein Analytics, then clicking Create Story

B.  

Copying text from company reports and pasting into Einstein Discovery

C.  

Uploading a Microsoft® PowerPoint® presentation

D.  

Entering notes about the data

A.  

Inherited security

B.  

Row-level security with security predicates

C.  

Field-level security

D.  

App level security

A.  

It instantly provides key metrics on open cases, average time to close, first contact resolution, and customer satisfaction.

B.  

It lets you drill down to more detailed dashboards, like agent performance, channel review, and telephony metrics.

C.  

It's available on desktop and mobile.

D.  

It's a great place to start your analysis.

E.  

All of the above.

A.  

Extract data from the case and account objects using "sfdcDigest" transformation, Q use "append" transformation to add case data with account data, store as a dataset, and then build a dashboard using the generated dataset

B.  

Extract data from the case and account objects using an ETL tool, use the ETL tool to y/ Q join account and case data, and then upload the data to Einstein Analytics. Build a dashboard using the ETL generated dataset

C.  

Extract data from the case and account objects using "sfdcDigest" transformation, y/ Q use "augment" transformation to add case data with account data, store as a dataset, and then build a dashboard using the generated dataset

D.  

Extract data from the case and account objects using "dataloader", create a CSV file Q with account and case data, and then upload the CSV as a dataset to Einstein Analytics. Build a dashboard using that dataset

A.  

Rest API

B.  

Salesforce Managed Package

C.  

Change Sets

D.  

Metadata API

A.  

Remove the outlier rows.

B.  

Nothing, because the field is not the outcome variable.

C.  

Change the method of binning to fixed width.

D.  

Remove the field because it has bad data

A.  

The model is very strong. A GINI coefficient of .535 shows that this model is very effective.

B.  

The model is pretty good; the accuracy rating of .7597 means we can predict both wins and losses at a fairly high rate.

C.  

The strength of the model cannot be determined with the metrics shown.

D.  

The model is very weak and doesn't provide useful predictions due to the low threshold.

A.  

How combinations of factors affect the CLV for Naval customers

B.  

How individual factors separately affect the outcome for Naval customers

C.  

What the relationship is between significant and insignificant factors as they impact CLV for Naval customers

D.  

How the chosen variable is related to all other factors in the model for Naval customers

A.  

Create a recipe to generate a new dataset from the "OpportunityWithAccount" and "CaseWithAccount" datasets aggregated by Account, and use the generated dataset in a lens.

B.  

Use "Compare Table" to join the "OpportunityWithAccount" and "CaseWithAccount" datasets using the "Accountld" and then aggregate by Account.

C.  

Set up Connect Data Sources in the UI between the "OpportunityWithAccount" and "CaseWithAccount" datasets.

D.  

Use a SAQL query to group and join the "OpportunityWithAccount" and "CaseWithAccount" datasets then generate the results.

A.  

Add Custom Domain

B.  

Filter by Range

C.  

Add Custom Range

D.  

Add Filter