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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Development Lifecycle | 15% | - Testing agents in Testing Center - Monitoring, adoption and optimization - Deployment: sandbox to production - Version control and maintenance |
| Topic 2: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types - Unstructured data processing: chunking, indexing |
| Topic 3: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Prompt management and user roles - Grounding techniques and best practices |
| Topic 4: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Agent API usage scenarios - Model Context Protocol (MCP) |
| Topic 5: AI Agents | 35% | - Agent User security and permissions - Deterministic controls: filters and variables - Topics and actions configuration - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack |
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NEW QUESTION # 64
A business stakeholder wants to use Al to generate a summary based on Data Cloud data. Which method(s) should the stakeholder use to access Data Cloud data from Prompt Builder?
Answer: C
Explanation:
The Prompt Builder and Data Cloud Integration Guide explains that Data Cloud information can be accessed directly through Data Cloud related lists or prompt-initiated flows, which fetch relevant data dynamically. The documentation states: "Prompt Builder supports retrieving Data Cloud data using related lists for contextual grounding or invoking flows that query Data Cloud objects at runtime. This enables AI prompts to generate summaries, recommendations, or insights directly from unified customer profiles."
NEW QUESTION # 65
Universal Containers' sales team engages in numerous video sales calls with prospects across the nation. Sales management wants an easy way to understand key information such as deal terms or customer sentiments. Which Einstein Generative AI feature should An Agentforce recommend for this request?
Answer: B
Explanation:
Einstein Conversation Insights analyzes sales calls (including video and voice) to capture key moments, deal terms, competitor mentions, and customer sentiment. It helps sales management quickly understand what was discussed without manually reviewing the entire call.This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.
NEW QUESTION # 66
An Agentforce needs to enable the use of Sales Email prompt templates for the sales team. The Agentforce Specialist has already created the templates in Prompt Builder. According to best practices, which steps should the Agentforce Specialist take to ensure the sales team can use these templates?
Answer: C
Explanation:
To enable Sales Email prompt templates:
Permission Set: Assign the Prompt Template User permission set to the sales team to grant access to use pre-built templates.
Feature Activation: Enable Sales Emails in Salesforce Setup to activate the integration between prompt templates and email workflows.
NEW QUESTION # 67
An Al Specialist is tasked with creating a prompt template for a sales team. The template needs to generate a summary of all related opportunities for a given Account. Which grounding technique should the Al Specialist use to include data from the related list of opportunities in the prompt template?
Answer: C
Explanation:
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agentforce Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
NEW QUESTION # 68
What is an appropriate use case for leveraging Agentforce Sales Agent in a sales context?
Answer: C
Explanation:
Agentforce Sales Agent is designed to let sales teams perform tasks via natural language commands, leveraging Salesforce data while adhering to policies. For example, agents can ask the AI to "update the opportunity stage to Closed Won" or "generate a quote," with the system enforcing validations and data security. This use case aligns with Salesforce's vision of conversational AI streamlining workflows without compromising compliance.
NEW QUESTION # 69
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