After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?
Correct Answer:
C
The Temperature hyperparameter controls the randomness of model outputs:
✑ Low Temperature (e.g., 0.2): More deterministic, consistent responses.
✑ High Temperature (e.g., 1.0): More creative, varied responses.
✑ Presence Penalty (Option A): Discourages repetition of tokens, unrelated to randomness.
✑ Variability (Option B): Not a standard hyperparameter in Einstein Studio.
References:
✑ Einstein Studio Documentation: Model Hyperparameters
✑ Explicitly states "Temperature adjusts the balance between predictable and random outputs."
An Agentforce Specialist needs to create a prompt template to fill a custom field named Latest Opportunities Summary on the Account object with information from the three most recently opened opportunities. How should the Agentforce Specialist gather the necessary data for the prompt template?
Correct Answer:
B
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account. The most robust and flexible way to achieve this is by using a Flow (Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by CreatedDate descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.
✑ Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they don??t natively support querying or aggregating related list data like the three most recent opportunities.
✑ Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer (perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the Opportunity object as a resource, prompt templates
don??t directly query related objects without additional logic (e.g., a Flow), making this incorrect.
✑ Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close Date) for the AI to summarize.
Thus, Option B is the correct method to gather the necessary data efficiently and accurately.
References:
✑ Salesforce Agentforce Documentation: "Integrate Flows with Prompt Templates" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_flow_prompt_integratio n.htm&type=5)
✑ Trailhead: "Build Flows for Agentforce" (https://trailhead.salesforce.com/content/learn/modules/flows-for-agentforce)
What is best practice when refining Agent custom action instructions?
Correct Answer:
A
When refining Agent custom action instructions, it is considered best practice to provide examples of user messages that are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.
✑ Option B (consistent phrases) can improve clarity but does not directly refine the
triggering logic.
✑ Option C (specifying a persona) is not as crucial as giving examples that illustrate how users will interact with the custom action.
For more details, refer to Salesforce's Agent documentation on building and refining custom actions.
An administrator is responsible for ensuring the security and reliability of Universal Containers' (UC) CRM data. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant information from a Salesforce record to be merged with the prompt. Which feature in the Einstein Trust Layer best supports UC's need?
Correct Answer:
B
Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI- generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.
Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model??s outputs are trustworthy and reliable for business use. The other options are less aligned with the requirement:
✑ Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.
✑ Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.
References:
✑ Salesforce Developer Documentation on Einstein Trust Layer
✑ Salesforce Security Documentation for AI and Data Privacy
Universal Containers (UC) needs to improve the agent productivity in replying to customer chats.
Which generative AI feature should help UC address this issue?
Correct Answer:
B
✑ Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.
✑ Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.
✑ Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.
Thus, Service Replies is the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.
Reference:
"Boost Productivity with Generative AI in Service Cloud | Salesforce Trailhead" .
