In an org, some of the Activity records do not have related Opportunity records. Additionally, there are Opportunity records that do not have related Activity records.
Which SAQL statement will fetch all Opportunities, even if they don't have associated Activities, and fetch related Activity data, if it exists?

• A.q = cogroup Opportunity by 'Id', Activities by 'Opportunityld';
• B.q = cogroup Opportunity by 'Id', Activities by 'Opportunityld' left;
• C.q = cogroup Opportunity by 'Id' right, Activities by 'Opportunityld';
• D.q = cogroup Opportunity by 'Id' left, Activities by 'Opportunityld';

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Exhibit:

A shipping company created a dataset. "Bookll," containing budget per region per month for the first six months of 2018. The dataset is shown in the graphic. Now, they want to create a lens showing the total budget for each region for each month. Every combination of region and month must be shown in the lens, even if there is no data.
How can an Einstein Consultant help this company build this lens?

• A.Use a SAQL query to create fake rows for any data that is missing data from the dataset.
• B.Use a "Compare Table" and use the "Show summary" option.
• C.Use a "Compare Table" and add a column leveraging the "Running Total" function.
• D.Use a SAQL query leveraging the "fill" statement with a "partition" parameter.

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A consultant built an Einstein Analytics dashboard for a company. The company then requested an enhancement to the dashboard and provided additional data that needs to be displayed. As a result, the consultant decides to "augment" the dataflow.
Which phrase describes this transformation?

• A.Add data based on the right-side grain
• B.Join data similar to an SQL statement
• C.Add data as a lookup relationship (left side is the lowest grain)
• D.Join data based on a many-to-many relationship

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A company wants to build a webpage that displays KPIs that can be derived from values in datasets stored in Einstein Analytics. How can an Einstein Analytics consultant derive those KPIs from those datasets?

• A.Export the dataset as a CSV file and load it to an external database.
• B.Use the Analytics External Data API and leverage the "query" resource.
• C.Use the Analytics REST API and leverage the -query" resource.
• D.Export the dataset as an XLS file and use the Einstein Analytics Connector for Excel.

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Why is it important to select the right root object when you use the dataset builder?

• A.The root object determines what app the dataset is saved in.
• B.The root object determines who can see the dataset.
• C.The root object determines what related objects you can add to the dataset.
• D.Only data from the root object can be included in the dataset.

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