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NEW QUESTION # 67
Universal Containers is implementing a customer verification process for its Service Agent where sensitive account information can only be accessed after the customer passes identity verification. The Agentforce Specialist needs to ensure this security rule is enforced deterministically, preventing the large language model from bypassing the verification requirement to execute the account lookup action.
What should the specialist configure to manage this deterministic behavior?
Answer: C
Explanation:
The correct answer is B because sensitive account lookup must be gated deterministically. A custom variable can store verification status, and an available when filter can make the lookup action invisible until that status is true. This prevents the LLM from even seeing or selecting the protected action before verification succeeds. Option A misuses Prompt Defense; prompt defense helps protect against unsafe prompt behavior, but it does not replace action-level authorization. Option C is unreliable because natural language instructions are still interpreted by the model and do not enforce execution control. Salesforce release guidance states that filters can be created for topics and actions so the agent can use them only when conditions are met. Variables are also used in filters and action control.
NEW QUESTION # 68
A data science team has trained an XGBoost classification model for product recommendations on Databricks. TheAgentforce Specialistis tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should theAgentforce Specialistset this up?
Answer: C
Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
* Create the Serving Endpoint in Databricks:
* The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
* Configure the Model Using Model Builder:
* After creating the endpoint, theAgentforce Specialistshould configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
* Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
* Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.
NEW QUESTION # 69
An Agentforce Service Agent, who has been successfully assisting customers with service requests in Salesforce, is now unable to help customers with issues related to a new product replacement process. The company recently implemented a custom Product Replacement object in Salesforce to track and manage these replacements. Which Agentforce Agent User change must be implemented to address this issue?
Answer: A
Explanation:
Why is "Permission Set Read Access" the correct answer?
If an Agentforce Service Agent is unable to assist customers with the new Product Replacement process, it is likely due to missing object permissions.
Key Considerations for Object Access in Agentforce:
Custom Objects Require Permission Set Access
The new Product Replacement object must be explicitly assigned to the agent's permission set.
Without Read access, the agent cannot view or interact with the object.
Ensuring Full Data Access for Agents
In Setup # Permission Sets, the admin should:# Grant Read access to the Product Replacement object# Ensure that related fields (e.g., status, replacement reason) are also accessible Aligning AI and Agent Workflows If Einstein AI is used to suggest solutions, the agent must have visibility into the Product Replacement object for context-aware responses.
Why Not the Other Options?
# A. The permission set group assigned to the Agent User needs to grant access to the Product Replacement flow.
Incorrect because flow permissions only control automation access, not direct object access.
If an agent cannot view the object, the flow will not be visible or usable.
# C. The profile assigned to the Agentforce Agent User needs AI training permission to the custom Product Replacement object.
Incorrect because AI training permissions relate to model learning and improvement, not object visibility.
Agentforce Specialist References
Salesforce AI Specialist Material confirms that permission sets control object-level access for Agentforce users.
NEW QUESTION # 70
At Horizon Insurance, the customer service team is reporting that the agent consistently routes claims intake conversations to the Policy Inquiry subagent, even though user intent clearly indicates a claims-related issue.
This misrouting persists despite the user clearly expressing the intent to discuss a claim. The Agentforce Specialist added guard conditions, but the problem persists.
What is most likely causing this incorrect routing in Horizon Insurance's agent?
Answer: B
Explanation:
The correct answer is C because persistent routing to the wrong subagent usually indicates semantic overlap in subagent purpose, description, or entry conditions. Agentforce uses subagent descriptions and instructions to determine which capability is relevant to the user's intent. If "Claims Intake" and
"Policy Inquiry" both describe insurance policy details, claims eligibility, or coverage questions too broadly, the reasoning engine can classify claims requests into the wrong subagent. Procedural instructions and Flows are not the normal root cause of intent classification failure. Global instructions can influence overall behavior, but they should not be used as the primary routing definition for competing subagents. Salesforce Agent Script guidance emphasizes that topic or subagent descriptions help the model understand when a topic is relevant during classification.
NEW QUESTION # 71
An Agentforce at Universal Containers is trying to set up a new Field Generation prompt template. They take the following steps.
1. Create a new Field Generation prompt template.
2. Choose Case as the object type.
3. Select the custom field AI_Analysis_c as the target field.
After creating the prompt template, the Agentforce Specialist saves, tests, and activates it. Howsoever, when they go to a case record, the AI Analysis field does not show the (Sparkle) icon on the Edit pencil. When the Agentforce Specialist was editing the field, it was behaving as a normal field.
Which critical step did the Agentforce Specialist miss?
Answer: C
Explanation:
For Field Generation prompt templates to display the Sparkle icon (indicating AI-generated content), the target field must be explicitly associated with the prompt template on the Lightning page layout. Even if the prompt template is activated, failing to add the field to the page layout and link it to the template will result in the field behaving as a standard field. Salesforce documentation emphasizes that page layout configuration is mandatory to enable AI-driven field interactions.
* Reactivating the layout (A) is unnecessary unless the layout itself was modified after activation.
* Case objects are supported for Field Generation (B is incorrect).
Reference:
Salesforce Help Article: Configure Field Generation Prompt Templates ("Associating Fields with Page Layouts" section).
Einstein GPT Implementation Guide: "Enabling AI-Generated Fields in Lightning Pages."
NEW QUESTION # 72
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