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What should an AI Specialist consider when using related list merge fields in a prompt template associated with an Account object in Prompt Builder?
When using related list merge fields in a prompt template associated with the Account object in Prompt Builder, the Activities related list is not supported due to it being a polymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.
Option B is incorrect because person accounts do not limit the availability of merge fields for the Account object.
Option C is irrelevant since even if no related lists are available at runtime, the prompt can still generate based on other available data fields.
For more information, refer to Salesforce documentation on supported fields and limitations in Prompt Builder.
An AI specialist wants to leverage Record Snapshots grounding feature in a prompt template.
What preparations are required?
To use theRecord Snapshots grounding featurein a prompt template, you mustcreate a field setthat includes all fields required for grounding. Field sets define which fields from an object are accessible to the AI model, ensuring the prompt template has structured data to generate contextually accurate responses. Salesforce documentation emphasizes that grounding relies on explicitly defined field sets to avoid exposing unintended data and to comply with security policies.
Page Layout configuration(A) controls UI organization but does not directly enable grounding.
Dynamic Forms(C) customize record pages dynamically but are unrelated to data grounding for prompts.
Einstein GPT Implementation Guide: 'Preparing Data for AI Prompts' (Field Set requirements).
Where should the AI Specialist go to add/update actions assigned to a copilot?
To add or update actions assigned to a copilot, an AI Specialist can manage this through several areas:
Copilot Actions Page: This is the central location where copilot actions are managed and configured.
Record Page for the Copilot Action: From the record page, individual copilot actions can be updated or modified.
Copilot Action Library Tab: This tab serves as a repository where predefined or custom actions for Copilot can be accessed and modified.
These areas provide flexibility in managing and updating the actions assigned to Copilot, ensuring that the AI assistant remains aligned with business requirements and processes.
The other options are incorrect:
B misses the Copilot Action Library, which is crucial for managing actions.
C includes the Copilot Detail page, which isn't the primary place for action management.
An AI Specialist is creating a custom action for Agentforce.
Which setting should the AI Specialist test and iterate on to ensure the action performs as expected?
When creating a custom action for Einstein Bots in Salesforce (including Agentforce),Action Instructionsare critical for defining how the bot processes and executes the action. These instructions guide the bot on the logic to follow, such as API calls, data transformations, or conditional steps. Testing and iterating on the instructions ensures the bot understands how to handle dynamic inputs, external integrations, and decision-making.
Salesforce documentation emphasizes thatAction Instructionsdirectly impact the bot's ability to execute workflows accurately. For example, poorly defined instructions may lead to incorrect API payloads or failure to parse responses. TheEinstein Bot Developer Guidehighlights that refining instructions is essential for aligning the bot's behavior with business requirements.
In contrast:
Action Name(A) is a static identifier and does not affect functionality.
Action Input(B) defines parameters passed to the action but does not dictate execution logic.
Thus, iterating onAction Instructions(C) ensures the action performs as expected.
Salesforce Help Article:Create Custom Actions for Einstein Bots
Einstein Bot Developer Guide: 'Custom Action Configuration Best Practices' (Section 4.3).
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?
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.
