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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Agentforce Data Library types |
| Topic 2: Development Lifecycle | 15% | - Testing agents in Testing Center - Version control and maintenance - Deployment: sandbox to production - Monitoring, adoption and optimization |
| Topic 3: AI Agents | 35% | - Agent User security and permissions - Deterministic controls: filters and variables - Topics and actions configuration - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee |
| Topic 4: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Topic 5: Prompt Engineering | 20% | - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt template creation: field generation, flex types - Prompt management and user roles |
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問題 #135
Universal Containers would like to route a service agent conversation to a human agent queue.
Which tool connects the service agent to the human agent queue for escalation?
答案：A
解題說明：
Why is Outbound Omni-Channel Flow the Correct Answer?
In Agentforce, when a service agent's conversation needs to be escalated to a human agent queue, Outbound Omni-Channel Flow is the appropriate tool to facilitate this process.
Key Features of Outbound Omni-Channel Flow in Agentforce:
Automates Escalation to a Human Agent Queue
It ensures that service requests are dynamically routed to the most appropriate human agent, based on availability, skills, and predefined business logic.
Seamless Transition from AI to Human Agents
When Einstein Copilot or another AI-powered assistant identifies a case that requires human intervention, Omni-Channel Flow automatically routes the conversation.
Ensures Proper Prioritization & Load Balancing
By leveraging Omni-Channel routing rules, the system assigns conversations efficiently, avoiding delays in customer service.
Integration with Agentforce and Service Cloud
Works directly with Salesforce Service Cloud to route cases to the appropriate agent queue.
問題 #136
Universal Containers (UC) is creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements. Which prompt template type should UC use and which consideration should UC review?
答案：C
解題說明：
Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template to populate a field with generated output, which directly aligns with the Field Generation prompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC's requirement.
Additionally, UC has enabled the Einstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas.
The consideration UC should review is whether Dynamic Fields is enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn't adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.
問題 #137
Universal Containers (UC) plans to answer questions based on similar cases that have been successfully resolved in the past. What should UC consider when implementing this approach?
答案：A
解題說明：
According to the AgentForce Data Configuration and Retrieval Guide, when an organization like Universal Containers wants to enable its AI agent to answer questions using historical case data, the correct implementation is to create an Unstructured Data Model Object (UDMO) based on the Case object, then index that data for retrieval.
The documentation clearly explains:
"When using previous case records to power AI-driven Q&A or similarity-based retrieval, create a UDMO mapped to the Case object. UDMOs allow the system to process and semantically index unstructured text fields such as Case Description, Resolution, and Comments, enabling the LLM to surface contextually similar resolved cases." This allows the AgentForce retrieval engine to perform semantic searches across historical support data, returning cases that are most contextually relevant to the user's query.
問題 #138
What is the primary advantage of creating an individual retriever instead of the default retriever?
答案：A
解題說明：
The AgentForce Data Cloud and Retrieval Configuration Guide explains that individual retrievers offer customization flexibility beyond the default retriever. The guide states: "Individual retrievers allow specialists to define filters, select specific fields for retrieval, and configure result limits, providing fine-grained control over data recall and relevance."
問題 #139
Universal Containers wants to implement a customer verification process where sensitive account information can only be accessed after the customer passes identity verification. The agent must enforce this security rule deterministically without allowing the large language model (LLM) to bypass the verification requirement. What should an Agentforce Specialist recommend as the best solution?
答案：C
解題說明：
The AgentForce Security and Deterministic Logic Guide specifies that sensitive actions must be gated through conditional filters linked to verification variables, not through natural language. It states: "For any process requiring secure, deterministic access, create a custom variable (e.g., IsCustomerVerified) that stores the verification status as a Boolean. Apply a filter expression to all protected actions (e.g., IsCustomerVerified = true). This ensures the LLM cannot bypass or alter access logic." This configuration ensures security and determinism because the execution of sensitive actions is programmatically enforced, not dependent on the LLM's understanding.
問題 #140
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