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| Vendor: | Salesforce |
|---|---|
| Exam Code: | ANC-301 |
| Exam Name: | Implement and Manage CRM Analytics |
| Exam Questions: | 115 |
| Last Updated: | January 7, 2026 |
| Related Certifications: | Salesforce Consultant, CRM Analytics and Einstein Discovery Consultant |
| Exam Tags: |
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Cloud Kicks (CK) wants to use CRM Analytics to analyze trends of its sales pipeline in order to accelerate the company's sales process. To do so, CK needs to know the average time an opportunity
spends in each stage. The data can be found in the Opportunity History object, but the value is not pre-calculated in Salesforce, so a consultant recommends using a recipe to calculate it.
How should the consultant use a recipe to calculate the average time an opportunity spends in each stage?
A consultant creates a CRM Analytics dashboard in a sandbox and it needs to be migrated into production.
What should the consultant use to complete the migration?
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?
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.
Universal Containers has a dashboard for sales managers. They need the ability to visualize the number of Closed Won opportunities by month, quarter, or year, and then display the result in a single chart. A CRM Analytics consultant creates a custom query to display three values:
ClosedDate_month, ClosedDate_ quarter, and ClosedDate_year.
What should the consultant do next?
A CRM Analytics administrator is working on deploying a dashboard and a dataset from a developer sandbox to a full sandbox. They have deployed the dataset via change set and manually copy-pasted the dashboard JSON into the target org. However, they notice that the conditional formatting and the widget-specific number formats have been lost in the target environment.
What is causing this issue?
When deploying a dataset and dashboard between environments in CRM Analytics, it's essential to include the Extended Metadata (XMD) file, which controls aspects such as conditional formatting and number formatting. In this case, the administrator manually copied the dashboard JSON but did not deploy the Analytics Dataset XMD, which leads to the loss of conditional formatting and widget-specific number formats in the target environment. Including the XMD ensures that all formatting and metadata are transferred correctly.
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