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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Topic 2: Development Lifecycle | 15% | - Deployment: sandbox to production - Testing agents in Testing Center - Monitoring, adoption and optimization - Version control and maintenance |
| Topic 3: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing |
| Topic 4: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt management and user roles - Prompt Builder usage and use cases - Grounding techniques and best practices |
| Topic 5: AI Agents | 35% | - Agent types: Service, Sales, Employee - Agent User security and permissions - Channel integration: digital experience, email, Slack - Topics and actions configuration - Deterministic controls: filters and variables - Agent architecture and reasoning engine |
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질문 # 141
An Agentforce implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce.
The Agentforce Specialist needs to configure the system to use the most accurate and up-to-date information for email generation. Which grounding technique should the Agentforce Specialist use?
정답：A
설명：
For Einstein Sales Emails to generate personalized follow-up emails, it is crucial to ground the email content with the most up-to-date and accurate information. Grounding refers to connecting the AI model with real-time data. The most appropriate technique in this case is Ground with Record Merge Fields. This method ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.
Record Merge Fields ensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.
Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.
Automatic grounding using Draft with Einstein is a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data like Record Merge Fields.
질문 # 142
Which object stores the conversation transcript between the customer and the agent?
정답：A
설명：
Why is "Messaging Session" the correct answer?
In Agentforce, the Messaging Session object stores the conversation transcript between the customer and the agent.
Key Features of the Messaging Session Object:
Stores the Entire Customer-Agent Conversation
The Messaging Session object maintains a record of the full chat history, including timestamps, messages, and interactions.
This ensures that past interactions can be referenced during follow-ups.
Supports AI-Powered Work Summaries
Einstein AI uses Messaging Sessions to generate summaries of chat interactions for agents.
These summaries are stored and accessible for later reference.
Links with Service Cloud for Case Resolution
If a conversation escalates into a case, the Messaging Session object can be linked to it.
This allows support teams to review the conversation history without switching contexts.
질문 # 143
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?
정답：C
설명：
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.
질문 # 144
In Model Playground, which hyperparameters of an existing Salesforce-enabled foundational model can An Agentforce change?
정답：B
설명：
In Model Playground, An Agentforce working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:
Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.
Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model's responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer to Salesforce AI Model Playground guidance from Salesforce's official documentation on foundational model adjustments.
질문 # 145
Universal Containers (UC) is preparing to use the Agentforce Testing Center to ensure the reliability of a new agent. UC has a CSV file with test cases and is reviewing the documentation to understand best practices and limitations. Which best practice should the company follow to avoid modifying CRM data while running tests in the Testing Center?
정답：C
설명：
According to the AgentForce Testing and Validation Guidelines, all automated or large-scale test runs in Testing Center should be executed in a sandbox environment to prevent any unintended modifications to live CRM data.
Running tests in production can trigger record updates, create cases, or call actions that alter live data, violating best practice standards for safe validation. Testing in a sandbox ensures the environment mirrors production logic while maintaining data isolation.
질문 # 146
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