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| Section | Weight | Objectives |
|---|---|---|
| AI Agents | 35% | - Deterministic controls: filters and variables - Topics and actions configuration - Agent User security and permissions - Agent architecture and reasoning engine - Channel integration: digital experience, email, Slack - Agent types: Service, Sales, Employee |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding |
| Prompt Engineering | 20% | - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt management and user roles - Prompt template creation: field generation, flex types |
| Development Lifecycle | 15% | - Testing agents in Testing Center - Monitoring, adoption and optimization - Deployment: sandbox to production - Version control and maintenance |
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NEW QUESTION # 65
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI- driven training content. However, users report that the AI frequently returns outdated documents.
Which corrective action should UC implement to improve content relevancy?
Answer: C
Explanation:
UC's issue is that their file upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud's vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter (Option B). In Agentforce, a custom retriever allows UC to define specific conditions-such as a filter on a "Last Modified Date" or similar timestamp field-to limit retrieval to documents updated within a recent period (e.g., last
6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.
NEW QUESTION # 66
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.
NEW QUESTION # 67
A business stakeholder wants to use Al to generate a summary based on Data Cloud data. Which method(s) should the stakeholder use to access Data Cloud data from Prompt Builder?
Answer: C
Explanation:
The Prompt Builder and Data Cloud Integration Guide explains that Data Cloud information can be accessed directly through Data Cloud related lists or prompt-initiated flows, which fetch relevant data dynamically. The documentation states: "Prompt Builder supports retrieving Data Cloud data using related lists for contextual grounding or invoking flows that query Data Cloud objects at runtime. This enables AI prompts to generate summaries, recommendations, or insights directly from unified customer profiles."
NEW QUESTION # 68
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 # 69
Universal Containers (UC) is looking to improve its sales team's productivity by providing real- time insights and recommendations during customer interactions. Why should UC consider using Agentforce Sales Agent?
Answer: C
Explanation:
Agentforce Sales Agent provides real-time insights and AI-powered recommendations, which are designed to streamline the sales process and help sales representatives focus on key tasks to increase conversion rates. It offers features like lead scoring, opportunity prioritization, and proactive recommendations, ensuring that sales teams can interact with customers efficiently and close deals faster.
NEW QUESTION # 70
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