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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Topic 2: Development Lifecycle | 15% | - Version control and maintenance - Monitoring, adoption and optimization - Testing agents in Testing Center - Deployment: sandbox to production |
| Topic 3: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types |
| Topic 4: AI Agents | 35% | - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables - Topics and actions configuration - Agent architecture and reasoning engine - Agent User security and permissions |
| Topic 5: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt management and user roles |
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NEW QUESTION # 270
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?
Answer: A
Explanation:
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.
NEW QUESTION # 271
An Agentforce agent must retrieve data from Salesforce and an external system. What is the recommended approach?
Answer: D
Explanation:
External data access should be handled via Apex or Flow integrations, not direct LLM training.
NEW QUESTION # 272
Universal Containers is indexing millions of product manuals where users may ask both structured queries (model numbers) and natural language questions (for example, "How do I reset my device?"). Which retrieval approach should the company use?
Answer: A
Explanation:
According to the AgentForce Retrieval Optimization Guide, when users ask both structured (exact) and unstructured (natural language) questions, the best practice is to use hybrid search.
The documentation states:
"Hybrid search combines the precision of keyword retrieval for structured terms, such as IDs or model numbers, with the semantic flexibility of vector search for natural language queries. This approach ensures both deterministic accuracy and contextual understanding."
NEW QUESTION # 273
An account manager is preparing for an upcoming customer call and wishes to get a snapshot of key data points from accounts, contacts, leads, and opportunities in Salesforce. Which feature provides this?
Answer: A
Explanation:
Sales Insight Summary aggregates key data points from multiple Salesforce objects (accounts, contacts, leads, opportunities) into a consolidated view, enabling account managers to quickly access relevant information for customer calls.
NEW QUESTION # 274
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: A
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 # 275
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