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CRM-Analytics-and-Einstein-Discovery-Consultant Test Dumps, CRM-Analytics-and-Einstein-Discovery-Consultant VCE Engine Ausbildung, CRM-Analytics-and-Einstein-Discovery-Consultant aktuelle Prüfung

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Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Prüfungsplan:

Thema
Einzelheiten

Thema 1

Einstein Discovery: This section unveils the magic of AI-driven insights and candidates' ability to analyze and choose one of the three types of predictions. It involves leveraging Einstein's advanced analytics capabilities to adjust data parameters, add or remove data and columns for the improvement of the model.

Thema 2

Analytics Dashboard Implementation: Here, consultants embark on a creative exploration of dashboard configuration, optimization of query performance using Dashboard Inspector, and using advanced functionality such as windowing.

Thema 3

Data Layer: In this comprehensive section, Salesforce consultants delve into the heart of data extraction and loading. It's all about showcasing a deep understanding of implementing refreshes for data syncs, performing data transformations, and implementing delivery management strategies in dataflows.

>> CRM-Analytics-and-Einstein-Discovery-Consultant Unterlage <<

CRM-Analytics-and-Einstein-Discovery-Consultant Pruefungssimulationen - CRM-Analytics-and-Einstein-Discovery-Consultant Prüfungsfrage

Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant dumps von PrüfungFrage sind die unentbehrliche Prüfungsunterlagen, mit denen Sie sich auf Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Zertifizierung vorbereiten. Der Wert dieser Unterlagen ist gleich wie die anderen Nachschlagsbücher. Diese Meinung ist nicht übertrieben. Wenn Sie diese Schulungsunterlagen zur Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Zertifizierung benutzen, finden Sie es wirklich.

Salesforce Certified CRM Analytics and Einstein Discovery Consultant CRM-Analytics-and-Einstein-Discovery-Consultant Prüfungsfragen mit Lösungen (Q18-Q23):

18. Frage
Universal Containers (UC) is using CRM Analytics to create two datasets.
* Dataset A: Contains a list of activities with an "activityID" dimension and a "userID" dimension
* Dataset B: Contains a list of users with a "userID" dimension
UC wants to delete all activities from Dataset A related to users in Dataset B.
How should the CRM Analytics consultant help UC achieve this?

A. Use the recipe node "delete" and set "userID" as the deletion ID.
B. Use the "update" transformation and utilize the "filter" node.
C. Use the combination of recipe transformations: "join" and "filter".

Antwort: C

Begründung:
In CRM Analytics, when dealing with two datasets, such as Dataset A (activities) and Dataset B (users), and you want to delete records from Dataset A based on the users listed in Dataset B, you would typically use a combination of a join and filter transformation in a recipe. The join transformation allows you to combine data from both datasets based on the shared userID dimension, and the filter transformation would then be used to delete or exclude any activities from Dataset A that are associated with the users from Dataset B. This approach ensures that only relevant activities remain in Dataset A after filtering out the unwanted ones.
Reference: CRM Analytics Recipes: Join and Filter Transformations

19. Frage
Universal Containers (UC) creates a dataset, "Book11", containing a budget per region per month for the first 6 months of the year, as shown in the graphic below. Now, UC wants 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 should a CRM Analytics consultant help UC build this lens?

A. Use a "Compare Table" and add a column leveraging the "Running Total" function.
B. Use a "fill" statement in SAQL query with a "partition" parameter.
C. Use a "Compare Table" and use the "Show Totals" option.

Antwort: A

20. Frage
Universal Containers (UC) is using CRM Analytics to create two datasets.
* Dataset A: Contains a list of activities with an "activityID" dimension and a "userID" dimension
* Dataset B: Contains a list of users with a "userID" dimension
UC wants to delete all activities from Dataset A related to users in Dataset B.
How should the CRM Analytics consultant help UC achieve this?

A. Use the recipe node "delete" and set "userID" as the deletion ID.
B. Use the "update" transformation and utilize the "filter" node.
C. Use the combination of recipe transformations: "join" and "filter".

Antwort: C

21. Frage
A story created has an AUC of .62.
What two things could be done to possible improve this AUC score?
Choose 2 answers

A. Add more rows of relevant data.
B. Segment the training set.
C. Change the order of the story insights to descending.
D. Run detect sentiment on the dataflow before creating the story.

Antwort: C,D

22. Frage
A company used Einstein Analytics to create two datasets:
1. "Opportunity With Account- dataset, which contains data from the Opportunity standard object augmented by the Account data.
2. "Case with Account" dataset. which contains data from the Case standard object augmented by the Account data.
The company wants to build a lens showing the total number of Opportunities and Cases per Account. How can a consultant help them build this lens'

A. Use a SAQL query to group and join the "OpportunityWithAccount" and "CaseWithAccount" datasets then generate the results.
B. Use "Compare Table" to join the "OpportunityWithAccount" and "CaseWithAccount" datasets using the "Accountld" and then aggregate by Account.
C. Create a recipe to generate a new dataset from the "OpportunityWithAccount" and "CaseWithAccount" datasets aggregated by Account, and use the generated dataset in a lens.
D. Set up Connect Data Sources in the UI between the "OpportunityWithAccount" and "CaseWithAccount" datasets.

Antwort: A

23. Frage
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