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ANC-201 Salesforce Building Lenses, Dashboards, and Apps in Einstein Analytics

  • Salesforce
  • 4 free sample questions

About this exam

ANC-201 is the Salesforce exam Salesforce Building Lenses, Dashboards, and Apps in Einstein Analytics. Paid practice material for this exam is not available yet, but you can try its free sample questions.

Exam code
ANC-201
Vendor
Salesforce
Practice questions
Not available yet
Free sample questions
4
Question bank last updated
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Try 2 sample questions

Taken from the free sample set for ANC-201.

Question 1

Data Alerts

For Review

Einstein detected possible issues in your data. Review alerts and address data issues to get better insights, predictions, and improvements. Learn more [icon]

High Correlation

Store explains 35% of the variation in DailyQuantity. Such a high correlation might indicate possible data leakage. Investigate Store to determine whether it contains the information you are trying to predict. If data leakage is found, exclude Store from the story.

[ ] Exclude Store [ ] Ignore alert

consultant is reviewing a model that is set to maximize the daily sales quantity of consumer products in stores, and they see this recommendation. Which action should the consultant take?

  1. Option A:
     Verify client expectations that Store is a strong predictor for daily sales quantity.
  2. Option B:
    Remove the Store field from the model definition, because that is the recommended action.
  3. Option C:
    Ignore alert; the explanation of variation is only 35%, which is below 50%
Show answer and explanation

Correct answer: A

Upon reviewing the data model and noticing the high correlation alert between 'Store' and daily sales quantity, the appropriate action is to verify with the client their expectations regarding the influence of the Store field on daily sales. Here’s the rationale: Understanding the Role of 'Store' in the Model: Before making any changes to the model, it's crucial to understand whether the 'Store' field is expected to be a strong predictor based on the business context. If the client expects that different stores inherently have different sales volumes due to factors like location, size, or customer base, this correlation may be both meaningful and desired. Potential Data Leakage: High correlation warnings can sometimes indicate data leakage, where a predictor (like 'Store') might inadvertently include information about the outcome variable (daily sales quantity). It's essential to verify whether this correlation makes sense logically or if it's skewing the model predictions. Client Consultation: Consulting with the client helps ensure that any modeling decisions align with their business knowledge and expectations. It’s about validating the model against real-world expectations and ensuring it remains a useful tool for decision-making. By taking these steps, the consultant not only adheres to best practices in data science by validating model inputs and their implications but also ensures that the model aligns with the client’s business strategies and operational realities. 

Question 2

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?

  1. Option A:
    As part of the recipe, use a formula on the Roleid fild to create an attribute called 'ParentRolelDs' on the dataset, and apply the following security predicate: 'ParentRolelDs' == ‘’$UserRoleId’’ || Owned\ == ‘$User,id\\,
  2. Option 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’ == "$User.Id".
  3. Option C:
    As part of the recipe, use a multi row formula on the Roleld field to create an attribute called 'ParentRoleIDs' on the dataset, and apply the following security predicate: “$User.UserRoleld” || ‘Ownerld’ == “$User.Id".
Show answer and explanation

Correct answer: B

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.
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