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Salesforce Tableau-CRM-and-Einstein-Discovery-Consultant - Salesforce Tableau CRM Einstein Discovery Consultant (SU24)

A consultant wants to understand what the important predictors are in the story.

What are two places where this information can be found?

Choose 2 answers

A.

In the Einstein Recommendations for story improvement. Ifthere are no recommendation to remove strongest predictors, the story doesn’t hold any Important predictors but only weak (probably unrelated) terms.

B.

In the Story Settings, where the strength of predictor fields is calculated and visualized. The consultant can sort the list of predictor fields accordingly.

C.

In the Model Deployment Wizard while selecting the actionable fields

D.

In the Model Metrics section, where the top predictors are listed

The Universal Containers company built three Einstein Discovery stories that they want to use inSalesforce to predict and maximize their revenue per customer. The stories are for every region where they have business: EMEA, AMER, and APAC.

How can a consultant help them deploy the three Einstein models to Salesforce?

A.

Segment the account data per region and deploy the same model to all segments.

B.

Deploy the same model to all accounts and set the region field as an actionable variable.

C.

Deploy the same model to all accounts and use an Apex trigger to call the appropriate prediction.

D.

Segment the account data per region and deploy the appropriate model for each segment.

A Tableau CRM consultant has just completed deployment of an analytic app containing a recipe plus several datasets and dashboards. While conducting post deployment a smoke test, the new datasets don't seem to have migrated.

What post migration step has likely been forgotten?

What post migration step has likely been forgotten?

A.

Apply security predicates on datasets

B.

Run the recipe

C.

Provide read access on the datasets

D.

Go to Analytics Settings in Setup and approve the deployment

A customer is reviewing a story that is set to maximize the daily sales quantity of consumer products in stores and sees this chart. The visualized tooltip belongs to the blue bar for San Francisco, reflecting, November daily sales quantities in that cityspecifically.

What two conclusions can be drawn from this insight?

A.

The average daily in SAN Francisco stores in November as 1601 items higher than the global average of 335.

B.

November sales are higher than in other months. This November-effect is thestrongest in San Francisco.

C.

The average daily quantity in San Francisco stores in November was 1239 items higher than the average of all other months in San Francisco.

D.

The average daily quantity in San Francisco stores in November was 1239 items higher than the average of all November sales in the country

What happens if you first disable Analytics, and then you re-enable Analytics later? Select 2

A.

User permissions are removed from each defined permission set if Analytics is disabled. (Missed)

B.

User permissions are not removed from each defined permission set if Analytics is disabled.

C.

You must define the permission sets again if Analytics is re-enabled. (Missed)

D.

You must not define the permission sets again if Analyticsis re-enabled.

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

A.

If no layouts are eligible for the mobile device, the first defined layout is used. (Missed)

B.

If no layouts are eligible for the mobile device, anerror message will be displayed.

C.

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)

D.

A layout for mobile is eligible for use when the devicemeets all the device properties set in the Layout panel.

E.

There are widgets that cannot be displayed on mobile layouts.

Results from an Einstein Discovary story are reviewed with a business user. They agree with the findings but noticed that none of the fields used in the storyhave a correlation value greater than 4%. The client is now concerned that the model may not be good enough to

next steps?

A.

Rerun and update the story with a different algorithm.

B.

Proceed with deployment if the model quality metric values ire sufficient.

C.

Edit the model accuracy settings and rerun the story.

D.

Identify additional data that may have a stronger relationship with the outcome variable.

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 chartfrom 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.

Look up the API name of the source field

C.

Find source and target step names

D.

Configure the results binding on the target step in the dashboard JSON.

What is a valid permission for a permission set?

A.

Enable Parental Control

B.

Always Sign Me Up for Dreamforce

C.

Sales Analytics Apps

D.

Access Sales Cloud Analytics Templates and Apps

What are predictive insights good for?

A.

Predicting outcomes that you don't actually have the right data for

B.

Exploring your existing data to see what already happened

C.

Drilling down into the underlying reasons behind a prediction

D.

Choosing between all possible outcomes for a single variable