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Free ANC301 Exam Braindumps (page: 3)

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PDF with 242 Questions

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

Answer(s): A

Explanation:

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.



Cloud Kicks has informed CRM Analytics developers that they have two scenarios with restricted row-level security.
The parameters being:

1. Non-CXOs and VPs working in EMEA can have access to EMEA records only.
2. CXOs and VPs should have access to all data irrespective of the region (APAC, EMEA, etc.).
Which sharing method works for this scenario?

  1. Create two sets of dashboards; one for EMEA, and one for CXOs and VPs while filtering the dashboard on the region.
  2. Use a field on the user record like Department/Region, and apply row-level security based on that field.
  3. Create two separate datasets; one for EMEA, and one for CXOs and VPs.

Answer(s): B

Explanation:

For Cloud Kicks' requirements regarding access to data based on roles and geographic regions, the most efficient and scalable approach is to implement row-level security using fields on the user record, like Department or Region. Here's the rationale for choosing this approach:
Scalability and Maintenance: By applying security rules based on user record fields, Cloud Kicks can manage access dynamically without needing to maintain multiple dashboards or datasets. This reduces administrative overhead and simplifies updates as roles or regional structures change. Flexibility: Using a field on the user record to control access allows for easy expansion or modification of security policies as new regions or roles are added. Simplicity: This method ensures a clear and straightforward security model that can be easily audited and understood by administrators and compliance teams.



A team of CRM Analytics developers has been working on an existing recipe to add new derived fields. The edited version has been failing ever since, and management is requesting that the dashboard show refreshed data while they work on the edits. How can the developers add new fields while keeping the dataset refreshed?

  1. A recipe for the new fields and when that is successful, add it to the existing recipe as a join node.
  2. Clone the existing recipe to add fields and roll back the original recipe to the last working version.
  3. Refresh the dataset after working hours to avoid the edited version from failing.

Answer(s): B

Explanation:

When faced with the need to continue refreshing data while developing new features in a recipe, the best practice is:
Clone the Existing Recipe: By cloning the recipe, developers can experiment with adding new fields and transformations without affecting the production data flow. This allows for testing and development in a sandbox-like environment.
Roll Back to a Stable Version: Rolling back the original recipe to the last stable version ensures that the production dashboards continue to receive refreshed data, maintaining business operations without disruption.
This approach not only ensures data continuity but also provides a safe environment to address any issues that may arise from new developments.



A user is able to access the dashboards, lenses, and datasets of a particular app but is unable to change the name of the specific app.
What is causing the issue?

  1. The user does not have Manager access for that app.
  2. The app name cannot be changed once created.
  3. The user does not have Editor access for that app.

Answer(s): A

Explanation:

In CRM Analytics, the ability to modify the name of an app or make other significant changes typically requires Manager access. This level of access is distinct from Editor or Viewer permissions, which may allow for modifications to contents within the app but not to the app's core properties like its name. Here's the reasoning:
Access Restrictions: Manager access is specifically designed to control structural changes within the app, including renaming the app, which is considered a higher privilege operation. Role-Based Access Control: This ensures that only users with the necessary permissions can make significant changes, protecting the integrity and configuration of the app. Ensuring users have the appropriate level of access based on their responsibilities is a fundamental aspect of managing security and functionality in CRM Analytics.






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