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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Development Lifecycle | 15% | - Deployment: sandbox to production - Monitoring, adoption and optimization - Version control and maintenance - Testing agents in Testing Center |
| Topic 2: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Topic 3: AI Agents | 35% | - Agent User security and permissions - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Topics and actions configuration |
| Topic 4: Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Agentforce Data Library types |
| Topic 5: Prompt Engineering | 20% | - Prompt management and user roles - Grounding techniques and best practices - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases |
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NEW QUESTION # 249
Universal Containers (UC) users are complaining that agent answers are not satisfactory. The agent is using PDF files as a knowledge source. How should UC troubleshoot this issue?
Answer: A
Explanation:
Why is "Verify the retriever's filter criteria and data source connection" the correct answer?
If agent answers are not satisfactory when using PDF files as a knowledge source, the issue is likely caused by:
Retriever misconfiguration
If filters are too broad or too restrictive, AI may fail to find relevant information.
Checking filter logic and retrieval scope helps improve accuracy.
Incorrect data source connection
If the retriever is not properly linked to the PDF storage location, it may fail to retrieve relevant information.
Ensuring a stable connection between Salesforce Data Cloud and the retriever prevents retrieval failures.
Parsing Issues with PDF Files
If PDFs are not properly indexed, AI may struggle to extract relevant content.
Ensuring structured document formatting improves AI comprehension.
NEW QUESTION # 250
What is one key purpose of action instructions when creating a custom agent action in Agentforce?
Answer: C
Explanation:
According to the AgentForce Action Design and Configuration Guide, action instructions serve as the directive text that informs the Reasoning Engine about when and how to invoke a specific action. The documentation states: "Action instructions guide the reasoning engine by describing the action's purpose, inputs, and when it should be selected. Clear, concise instructions improve the LLM's accuracy in mapping user intent to the correct action."
NEW QUESTION # 251
Universal Containers (UC) stores case details and updates in several custom fields and custom objects related to the case. UC would like its Agentforce Service Agent to be able to provide information in these fields and related records as part of an answer back to its customers when the customer is asking for updates. Which best practice should UC follow to grant access to this information for the Agentforce Service Agent?
Answer: C
Explanation:
Per the AgentForce Security and Permission Management Guide, the
AgentForceServiceAgentUserPsg (Permission Set Group) controls access privileges for Service Agents, including which Salesforce objects, fields, and related data they can read or interact with.
When extending an agent's access to additional custom fields or related objects, the documented best practice is to update the existing permission set group assigned to that agent type rather than creating new or profile-based permissions. This approach maintains centralized permission governance, ensures license alignment, and avoids conflicts or redundancy across multiple permission layers.
NEW QUESTION # 252
How does Secure Data Retrieval ensure that only authorized users can access necessary Salesforce data for dynamic grounding?
Answer: C
Explanation:
Secure Data Retrieval enforces Salesforce's security model by dynamically grounding data access in the permissions of the user executing the prompt. This ensures compliance with CRUD (Create, Read, Update, Delete) and FLS (Field-Level Security) settings, preventing unauthorized access to sensitive data. For example, if a user lacks access to a specific object or field, the AI model cannot retrieve it for dynamic grounding.
NEW QUESTION # 253
Universal Containers, dealing with a high volume of chat inquiries, implements Einstein Work Summaries to boost productivity. After an agent-customer conversation, which additional information does Einstein generate and fill, apart from the "summary"'
Answer: A
Explanation:
Einstein Work Summaries automatically generate concise summaries of customer interactions (e.g., chat transcripts). Beyond the "summary" field, it extracts and populates Issue (key problem discussed) and Resolution (action taken to resolve the issue). These fields help agents and upervisors quickly grasp the conversation's context without reviewing the full transcript.
NEW QUESTION # 254
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