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100% Pass 2025 Salesforce Fantastic CRM-Analytics-and-Einstein-Discovery-Consultant: Exam Salesforce Certified CRM Analytics and Einstein Discovery Consultant Revision Plan

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Salesforce CRM-Analytics-and-Einstein-Discovery-Consultant Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 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.

Topic 3

Admin
Configuration: This topic takes Salesforce consultants on a journey through the enablement of CRM Analytics. It tests their ability to design a solution that is suitable for data sync
dataflows
recipe limits.

Topic 4

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.

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q87-Q92):

NEW QUESTION # 87
Refer to the exhibit.

Universal Containers has a dashboard for Sales Managers to visualize the YoY Growth of their customers- The formula used is: YoY = [(This Year - Last Year) / Last Year] % Based on the graphic, when there is no account in the Last Year column, the YoY Growth shows null results. The Sales Managers want to replace it with 100% value.
What is the correct function to use?

A. number_to_string()
B. substr()
C. coalesce()
D. replace()

Answer: C

NEW QUESTION # 88
A new picklist value was added for the Category field on the Account object. This field is already added as part of the Account object data sync and the respective recipe that uses this field.
The CRM Analytics team reports that when they start the recipe it runs successfully with no errors or warnings, but they are unable to See this new values on their existing dashboards.
What is the oridi4 of this issue?

A. The user who runs the dataflow/recipe does not have access to the field.
B. There are no records in Salesforce with this new plicklist value.
C. The Integration User profile does not have access to the field.

Answer: B

NEW QUESTION # 89
A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "*&**(&*(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?

A. The column header is auto-updated to "Column" + column number.
B. The column header Is set to *&**(&*(%.
C. The column header is prefixed with "X" upon upload.

Answer: C

Explanation:
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules. Headers containing non-alphanumeric characters, such as "&**(&(%", will automatically be adjusted. Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with "X" to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.

NEW QUESTION # 90
Which of these is not a method for controlling record-level access?

A. Sharing Rules
B. Profiles
C. Organization-Wide Defaults
D. Role Hierarchy

Answer: B

Explanation:
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

NEW QUESTION # 91
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?

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

Explanation:
In addressing the requirements of Universal Containers to ensure proper visibility of order data across different levels of the sales hierarchy, the use of a security predicate based on role hierarchies is paramount.
Here's why Option B is the ideal approach:
* Flatten Operation on Role Hierarchy: This operation is essential as it allows for the creation of a simplified or "flattened" view of the hierarchical relationships within the organization. This flattened view enables the dataset to understand and respect the hierarchical structure in security implementations.
* Creating a Multi-value Attribute ('ParentRoleIDs'): By creating this attribute, the recipe can hold multiple role IDs that a particular user has visibility permissions for. This is crucial in a hierarchical organization like UC where data visibility needs to cascade down the hierarchy.
* Security Predicate: The predicate ('ParentRoleIDs' == "$User.UserRoleld" || 'TeamMember.Id' ==
'$User.Id' || 'Ownerld' == "$User.Id") effectively enforces that:
* A user can see all orders where their role matches any of the role IDs in the 'ParentRoleIDs' list (hierarchical visibility).
* A user can see all orders where they are specifically listed as a team member.
* A user can see all orders where they are the owner.
This approach aligns with best practices for implementing row-level security in CRM Analytics, ensuring data visibility is managed correctly according to the defined organizational hierarchy and individual data ownership.

NEW QUESTION # 92
......

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