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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt management and user roles - Prompt Builder usage and use cases - Grounding techniques and best practices |
| Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing |
| Development Lifecycle | 15% | - Monitoring, adoption and optimization - Version control and maintenance - Testing agents in Testing Center - Deployment: sandbox to production |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| AI Agents | 35% | - Deterministic controls: filters and variables - Agent architecture and reasoning engine - Channel integration: digital experience, email, Slack - Agent User security and permissions - Agent types: Service, Sales, Employee - Topics and actions configuration |
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NEW QUESTION # 114
What is An Agentforce able to do when the "Enrich event logs with conversation data" setting in Agent is enabled?
Answer: B
Explanation:
When the "Enrich event logs with conversation data" setting is enabled in Agent, it allows An Agentforce or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.
This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement.
NEW QUESTION # 115
A data science team has trained an XGBoost classification model for product recommendations on Databricks. The Agentforce Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the Agentforce Specialist set this up?
Answer: B
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, the Agentforce Specialist should configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
NEW QUESTION # 116
An Agentforce agent is designed to recommend next steps but NOT execute them automatically.
What is this an example of?
Answer: C
Explanation:
Advisory agents recommend actions while leaving execution to humans.
NEW QUESTION # 117
Universal Containers needs to restrict access to refund processing actions so only customers with Active account status can initiate refunds. How should an Agentforce Specialist apply the restriction deterministically?
Answer: C
Explanation:
According to the AgentForce Action Orchestration and Control Logic Guide, deterministic restrictions on action execution should be implemented using context variables and conditional filters.
By creating a context variable (e.g., AccountStatus) that pulls the customer's current status and applying a conditional filter that limits execution to cases where AccountStatus = "Active", the refund action can be programmatically restricted. This ensures the agent can only trigger the refund flow when conditions are met, providing both consistency and governance over sensitive actions.
NEW QUESTION # 118
What does it mean when a prompt template version is described as immutable?
Answer: B
Explanation:
When a prompt template version is immutable, it means that once the version is activated, it cannot be edited or modified. This ensures consistency in production environments where changes could disrupt workflows.
NEW QUESTION # 119
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