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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 20% | - Prompt management and user roles - Prompt template creation: field generation, flex types - Grounding techniques and best practices - Prompt Builder usage and use cases |
| Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid |
| Development Lifecycle | 15% | - Version control and maintenance - Testing agents in Testing Center - Deployment: sandbox to production - Monitoring, adoption and optimization |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| AI Agents | 35% | - Agent User security and permissions - Agent types: Service, Sales, Employee - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Topics and actions configuration |
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NEW QUESTION # 68
Universal Containers wants to assign agents to improve department efficiency. Which configuration ensures the right tasks are handled by the right agents?
Answer: C
Explanation:
According to the AgentForce Product Overview and Deployment Guide, Salesforce recommends using purpose-built agents to maximize efficiency across departments. The documentation states:
"Each AgentForce agent type is optimized for a specific function - SDR Agent for sales development and lead nurturing, Service Agent for customer service and support cases, and Employee Agent for internal HR, IT, and productivity tasks." This separation ensures that each team benefits from a domain-specific agent equipped with the correct data access and actions.
NEW QUESTION # 69
When creating a custom retriever in Einstein Studio, which step is considered essential?
Answer: C
Explanation:
In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:
Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
NEW QUESTION # 70
Universal Containers (UC) has configured a data library and wants to restrict indexing of knowledge articles to articles which are only publicly available in their knowledge base, UC also wants the agent to link sources that the large language model (LLM) grounded its response on.
Which settings should help UC with this?
Answer: A
Explanation:
According to the AgentForce Data Library Configuration Guide, administrators can restrict indexing and retrieval of Knowledge articles to publicly available ones and enable source visibility for LLM-grounded responses. The documentation states:
"Within the data library settings, under Knowledge Settings, enable `Use Public Knowledge Articles' to ensure only publicly visible content is indexed. To display citations, enable `Show Sources' so the agent links the specific articles or data records used to ground its response."
NEW QUESTION # 71
What is best practice when refining Agent custom action instructions?
Answer: A
Explanation:
When refining Agent custom action instructions, it is considered best practice to provide examples of user messages that are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.
NEW QUESTION # 72
Universal Container's internal auditing team asks An Agentforce to verify that address information is properly masked in the prompt being generated. How should the Agentforce Specialist verify the privacy of the masked data in the Einstein Trust Layer?
Answer: A
Explanation:
The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the Agentforce Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is processed and masked in AI activities.
NEW QUESTION # 73
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