Question 1

A dataset is ready for analysis in Einstein Discovery.
What is the first step a consultant should take after the data is loaded?

Options :

A : Identify data types and define the appropriate data as numeric.

B : Analyze data columns and decide which, if any, to drop.

C : Produce a graph of the data to analyze.

D : Load the data into Einstein Analytics

Answer: A

Question 2

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

Options :

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, an error 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 device meets all the device properties set in the Layout panel.

E : There are widgets that cannot be displayed on mobile layouts.

Answer: A,C,D

Question 3

The Vice President of Sales wants to create a 'Pipeline Trending' dashboard in Einstein Analytics that displays specific colors depending on the values of the fields.
Which two techniques can a consultant use to help address this requirement? Choose 2 answers

Options :

A : Use an XMD file and assign colors to each field value.

B : Use SAQL queries to calculate a color field and assign it to each field value.

C : Use a compute Expression to calculate a color field and assign it to each field value.

D : Use the 'fields' menu when exploring the dataset and assign colors to each field value.

Answer: A,D

Question 4

Universal Containers (UC) is rolling out CRM Analytics to its field sales that include dashboards withorder data from an external source. UC has a well-defined role hierarchy where everyone is assigned to an appropriate node on the hierarchy. In addition, the order data has a reference to a Salesforce opportunity. An individual sales rep should be able to view all orders that they own or as part of the account team or opportunity team. The sales manager should be able to view all orders for the entire sales team. Similarly, the VP of sales should be able to view orders for everyone who rolls up in that hierarchy. The dataset has a field called Ownerld which represents the order owner. Given this information, how should a CRM Analytics consultant implement the above requirements?

Options :

A :

As part of the recipe, use a formula on the Roleid fildto create an attribute called 'ParentRolelDs' on the dataset, and apply the following security predicate: 'ParentRolelDs' ==‘’$UserRoleId’’ || Owned\ == ‘$User,id\\,

B :

As part of the recipe, use the flatten operation on the role hierarchy, create a multi-value attribute called 'ParentRoleIDs' on the dataset, and apply the following security predicate: 'ParentRoleIDs' == "$User. UserRoleld" || 'TeamMember.Id’ '$User, Id" || 'Ownerld’ == "$User.Id".

C :

As part of the recipe, use a multi row formula on the Roleld field to create an attribute called 'ParentRoleIDs' on the dataset, and apply the following security predicate:“$User.UserRoleld” || ‘Ownerld’ == “$User.Id".

Answer: B

Question 5

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'

Options :

A : q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (dateCols=('Date_Year, Date_Quarter', "Y-Q"), partition=Region', ignoreLast=true);

B : q = timeseries q generate 'sum_Sales' as Forecasted_Sales' with (Iength = 12, dateCols=('Date_Year', 'Date_Month". "Y-M"), partition='Region');

C : q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region', seasonality=4);

D : q = timeseries q generate 'sum_Sales' as Torecasted_Sales' with (dateCols=('Date_Year', Date_Quarter', "Y-Q"), partition='Region');

Answer: B

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