2022 Latest Tableau-CRM-Einstein-Discovery-Consultant dumps – Instant Download PDF [Q10-Q30]

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2022 Latest Tableau-CRM-Einstein-Discovery-Consultant dumps – Instant Download PDF

Updated Verified Tableau-CRM-Einstein-Discovery-Consultant Downloadable Printable Exam Dumps

Q10. An Einstein Consultant receives a request from the Marketing department to help them understand lead conversion. Presently, they are unaware of the percentage of leads that get converted to sales. They hope to view results by account manager, value, and quarter. The data is there, so the consultant can add it to the marketing dashboard.
How should this metric be calculated?

Create a new step in the dashboard using a compare table and define a formula.

Create a formula field on the lead object in Salesforce and add it to the dataset.

Create a computeExpression in the dataflow.

Create a new step in the dashboard using a compare table and the running total function.

Q11. Trending data limits: Maximum number of trended datasets per user and rows per snapshot:

5 per user, 200,000 per snapshot

10 per user, 100,000 per snapshot

10 per user, 200,000 per snapshot

5 per user, 100,000 per snapshot

Q12. What is a keyword in SAQL? And what case must it be in?

A keyword is basically just a function in SAQL, like group, foreach, etc. And it has to be lowercase.

Q13. In the list of insights, the first insights that you see are the ones that explain, statistically, the most variation in the outcome variable. The insights that appear later, as you scroll through the story, explain variables that, statistically, account for less of the variation in the outcome variable.
When you select one variable, what are you comparing?

Selected variable with CLV

Selected variable average with the mean

Selected variable with a different variable

Selected variable average with the filtered average

Selected variable with all story data

Q14. Which set of statements generates monthly amount on a cumulative basis annually?
A)

B)

C)

D)

Option A

Option B

Option C

Option D

Q15. Concurrent queries per user?

Q16. What does the Division is Naval section of the waterfall graph tell you?

How combinations of factors affect the CLV for Naval customers

How individual factors separately affect the outcome for Naval customers

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

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

Reference:
https://trailhead.salesforce.com/en/content/learn/modules/dm_einstein_discovery_advanced_stories/dm_understand_why_it_happened_insights

Q17. Which of the following are included in the template object?

A,C

ui.json

variables.json

A,B and C

app.json

Q18. An Einstein Analytics team reports that when they start their dataflow it runs successfully with no errors or warnings, but one of the fields does not return values when it is queried.
What can be the origin of this issue7

The field does not contain any data in Salesforce.

The user who runs the dataflow does not have access to the field.

The “Integration User Profile” does not have access to the field.

The “”Security User Profile” does not have access to the field.

Q19. Which of these is not a method for controlling record-level access?

Profiles

Sharing Rules

Organization-Wide Defaults

Role Hierarchy

Reference:
https://help.salesforce.com/articleView?id=bi_security_datasets_row_level.htm
https://help.salesforce.com/articleView?id=managing_the_sharing_model.htm&type=5

Q20. The client is trying to create a SAQL step to predict sales in each sales region. They cannot get the query to return any results, but have identified that the error is in the time series statement. They have asked an Einstein Consultant to review the following query and fix any errors.

Which timeseries statement will fix the query’

q = timeseries q generate ‘sum_Sales’ as Forecasted_Sales’ with (dateCols=(‘Date_Year, Date_Quarter’, “Y-Q”), partition=Region’, ignoreLast=true);

q = timeseries q generate ‘sum_Sales’ as Forecasted_Sales’ with (Iength = 12, dateCols=(‘Date_Year’, ‘Date_Month”. “Y-M”), partition=’Region’);

q = timeseries q generate ‘sum_Sales’ as Torecasted_Sales’ with (dateCols=(‘Date_Year’, Date_Quarter’, “Y-Q”), partition=’Region’, seasonality=4);

q = timeseries q generate ‘sum_Sales’ as Torecasted_Sales’ with (dateCols=(‘Date_Year’, Date_Quarter’, “Y-Q”), partition=’Region’);

Q21. In what order does Einstein Discovery present the insights that it uncovers?

Alphabetical, in ascending order

Alphabetical, in descending order

Insights that explain the most variation in the outcome variable, in ascending order

Insights that explain the most variation in the outcome variable, in descending order

Q22. A small Business Intelligence team is overwhelmed with multiple requests to create new dashboards. An option is to use layout templates to help with dashboard development.
What are two advantages of using layout templates? Choose 2 answers

Layout templates are equipped with design best practice, such as optimally positioned KPIs and filters

Layout templates are fast to create because they are fixed and read-only

Layout templates offer a consistent end user experience

Layout templates offer a universal layout thus reducing the hours spent on design

Q23. 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?

The list, chart, and table steps must share a common name.

The list, chart, and table steps must share common dimensions from different datasets.

The list, chart, and table steps must share the same dataset.

Chart and table steps must have their own list widgets.

Q24. Where can you view the status of a CSV upload in Analytics?

The data manager

The Analytics home page

The Create Dataset page

The Setup menu

Q25. Max number of dataflow definitions (with data sync enabled)

Q26. The Universal Containers company uses Einstein Analytics to build dashboards for different departments: Sales, Service, and Marketing. Users for the same department have the same role and need to have access to the same dashboards. Dashboards for different departments use some common datasets with the same row-level security.
How can an Einstein Consultant address this need?

Create one application for each department and use roles to share applications.

Create one application for each department, put common datasets in the shared app, and use profiles to share applications.

Create one application for each department, put common datasets in the shared app, and use roles to share applications.

Create one application for each department, put common datasets in the shared app, and use permission sets to share applications.

Q27. Creating an Einstein Discovery story involves:

Selecting a Dataset in Einstein Analytics, then clicking Create Story

Copying text from company reports and pasting into Einstein Discovery

Uploading a Microsoft® PowerPoint® presentation

Entering notes about the data

Q28. What is another name for the type of insight that examines how one variable explains variation of the outcome variable?

First-order analysis

Second-order analysis

Third-order analysis

Spectrum analysis

Object-oriented analysis

Q29. 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

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

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

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)

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

There are widgets that cannot be displayed on mobile layouts.

Reference:
https://trailhead.salesforce.com/en/content/learn/modules/wave_dashboard_designer_basics/wave_take_analytics_on_the_go

Q30. 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?

This dataset is too big and cannot be used in Einstein Discovery. Request a new dataset with fewer predictors.

Build the story with all the predictors and indicate that Einstein Discovery should show the top predictors.

Go back to the data preparation and reduce the number of fields to less than 30 in order to produce a story.

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.

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

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