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Salesforce Consultant Changed Tableau-CRM-and-Einstein-Discovery-Consultant Questions

Salesforce Tableau CRM Einstein Discovery Consultant (SU24) Questions and Answers

Question 25

A dashboard dataset is growing and the Einstein Analytics consultant notices an impact on performance. The consultant needs to make a few adjustments.

Which three actions can the consultant take to improve dashboard performance? Choose 3 answers

Options:

A.

Reorganize the dashboard widgets.

B.

Distribute steps among separate pages.

C.

Use SAQL code to join datasets at runtime.

D.

Replace separate step filters with a global filter.

E.

Move calculations to a dataflow.

Question 26

Which of the following is true about the Service Analytics Overview dashboard?

Options:

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.

Question 27

When creating a story in Einstein Discovery, do all potential collinear fields need to be removed before executing the build story'5

Options:

A.

No. Einstein Discovery is impervious to collinearity, so the story and subsequent model will be fine.

B.

No. Although it is ideal to eliminate collinearity as soon as possible, Einstein will give a warning post-build and the ridge regression will prevent collinearity from over-fitting.

C.

yes. If all collinear variables are not excluded, the model will over-fit and not make any sense.

D.

Yes. If the collinear variables are not removed, the Einstein Discovery model build will fail.

Question 28

A consultant is creating a Churn Prediction model to identify customers who are not likely to renew their contract.

What is the appropriate action to take?

Options:

A.

Replace nulls in the Churn Reason filed as ‘No reason given’’,

B.

Exclude active customers (customer who have not churned) from the training dataset since their Churned) from the training dataset since their Churn Reason field are nulls.

C.

For the Churn Reason field, enable the ‘’This variable contains sensitive data’’ box because it may contain sensitive customer behavior.

D.

Exclude the Churn reason field from the dataset.