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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Development Lifecycle | 15% | - Testing agents in Testing Center - Version control and maintenance - Deployment: sandbox to production - Monitoring, adoption and optimization |
| Topic 3: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Model Context Protocol (MCP) - Agent-to-agent communication |
| 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: AI Agents | 35% | - Deterministic controls: filters and variables - Agent User security and permissions - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Topics and actions configuration - Agent types: Service, Sales, Employee |
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NEW QUESTION # 283
After a successful implementation of Agentforce Sates Agent with sales users. Universal Containers now aims to deploy it to the service team. Which key consideration should the Agentforce Specialist keep in mind for this deployment?
Answer: A
Explanation:
When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:
Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.
NEW QUESTION # 284
An Agentforce wants to include data from the response of external service invocation (REST API callout) into the prompt template. How should the Agentforce Specialist meet this requirement?
Answer: C
Explanation:
An Agentforce wants to include data from the response of an external service invocation (REST API callout) into a prompt template. The goal is to incorporate dynamic data retrieved from an external API into the AI-generated content.
Solution:
Use External Service Record Merge Fields
External Service Integration:
Definition: External Services in Salesforce allow the integration of external REST APIs into Salesforce without custom code.
Registration: The external service must be registered in Salesforce, defining the API's schema and methods.
External Service Record Merge Fields:
Purpose: Enables the inclusion of data from external service responses directly into prompt templates using merge fields.
Functionality:
Dynamic Data Inclusion: Allows prompt templates to access and use data returned from REST API callouts.
Merge Fields Syntax: Use merge fields in the prompt template to reference specific data points from the API response.
Implementation Steps:
Register the External Service:
Use External Services to register the REST API in Salesforce.
Define the API's schema, including methods and data structures.
Create a Named Credential:
Configure authentication and endpoint details for the external API.
Use External Service in Flow:
Build a Flow that invokes the external service and captures the response.
Ensure the flow outputs the necessary data for use in the prompt template.
Configure the Prompt Template:
Use External Service Record merge fields in the prompt template to reference data from the flow's output.
Syntax Example: {{flowOutputVariable.fieldName}}
NEW QUESTION # 285
An Agentforce needs to create a Sales Email with a custom prompt template. They need to ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should the Agentforce Specialist obtain related items?
Answer: A
Explanation:
To ground a sales email on Opportunity Products, Events near the customer, and Tone and voice examples, the Agentforce Specialist should use a prompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.
NEW QUESTION # 286
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 # 287
The Agentforce Specialist of Northern Trail Outfitters reviewed the organization's data masking settings within the Configure Data Masking menu within Setup. Upon assessing all of the fields, a few additional fields were deemed sensitive and have been masked within Einstein's Trust Layer.
Which steps should the Agentforce Specialist take upon modifying the masked fields?
Answer: B
Explanation:
After modifying masked fields in Einstein's Trust Layer, the next important step is to test and confirm that the responses generated by prompts utilizing the newly masked data still meet quality standards. This ensures that masking sensitive information does not negatively impact the usefulness or accuracy of the AI-generated content. Thorough testing helps identify any issues in prompt performance that could arise due to masking, and adjustments can be made if needed.
NEW QUESTION # 288
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