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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt management and user roles - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases |
| Development Lifecycle | 15% | - Testing agents in Testing Center - Monitoring, adoption and optimization - Version control and maintenance - Deployment: sandbox to production |
| Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding - Agentforce Data Library types - Unstructured data processing: chunking, indexing |
| AI Agents | 35% | - Agent User security and permissions - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Topics and actions configuration |
| Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Model Context Protocol (MCP) - Agent API usage scenarios |
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NEW QUESTION # 211
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response. Which information does the Resolution text provide?
Answer: A
Explanation:
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs:
Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
NEW QUESTION # 212
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production. Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?
Answer: B
Explanation:
Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
The Agentforce Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.
What Can Actually Be Deployed from Sandbox to Production?
Custom Fields: Metadata that is easily created in sandbox and then deployed.
Quick Actions: Also metadata-based and can be deployed from sandbox to production.
Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.
NEW QUESTION # 213
Universal Containers implemented Agentforce for its users. One user complains that an Agent is not deleting activities from the past 7 days. What is the reason for this issue?
Answer: B
Explanation:
Universal Containers (UC) uses Agentforce, a specialized AI-driven assistant for Salesforce. A user reports that an Agent is unable to delete recent activities.
Why Agentforce Cannot Delete Records
Agentforce's Standard Actions: Agentforce typically has predefined or "standard" actions like Create, Update, or Summarize records. However, a standard Delete Record action is not part of the default set of Agentforce actions.
Implication: If Agentforce has no built-in delete functionality, it cannot remove activities-even if the user has permission to delete them in the Salesforce UI.
NEW QUESTION # 214
Universal Containers (UC) wants to implement an AI-powered customer service agent that can:
- Retrieve proprietary policy documents that are stored as PDFs.
- Ensure responses are grounded in approved company data, not generic
LLM knowledge.
What should UC do first?
Answer: A
Explanation:
To implement an AI-powered customer service agent that retrieves proprietary policy documents (stored as PDFs) and ensures responses are grounded in approved company data, UC must first establish a foundation for the AI to access and use this data. The Agentforce Data Library (Option A) is the correct starting point. A Data Library allows UC to upload PDFs containing policy documents, index them into Salesforce Data Cloud's vector database, and make them available for AI retrieval. This setup ensures the agent can perform Retrieval-Augmented Generation (RAG), grounding its responses in the specific, approved content from the PDFs rather than relying on generic LLM knowledge, directly meeting UC's requirements.
NEW QUESTION # 215
Which scenario BEST justifies invoking Apex instead of Flow from an Agentforce agent?
Answer: D
Explanation:
Apex is better suited for complex logic, integrations, and custom error handling.
NEW QUESTION # 216
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