Selected Salesforce-AI-Specialist AI Associate Questions Answers

For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.

Option B is the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.

Option A (customer sentiment) is typically handled through NLP models, while Option C (product popularity) is more of a marketing analysis use case.

References:

Salesforce Einstein Studio Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview

To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu. This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.

Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.

Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.

References:

Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm

Einstein Copilot is designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team at Universal Containers, this is the perfect solution to create dynamic and relevant email content. By leveraging Einstein Copilot, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.

Option A is correct as Einstein Copilot helps generate real-time, personalized content based on comprehensive data about the customer.

Option B refers more to Einstein Analytics or Marketing Cloud Intelligence, and Option C deals with automation, which isn't the primary focus of Einstein Copilot.

References:

Salesforce Einstein Copilot Overview: https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm

To enable Universal Containers service agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is to create a custom copilot action that calls the flow. This action will allow Einstein Copilot to interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.

Option B is correct because it enables integration between Einstein Copilot and the flow that connects to Oracle ERP.

Option A (Flex prompt template) is more suited for static responses and not for invoking flows.

Option C (Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.

References:

Salesforce Einstein Copilot Actions: https://help.salesforce.com/s/articleView?id=einstein_copilot_actions.htm