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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing, Deployment, and Operations | 20% | - Lifecycle management
|
| Topic 2: AI Agents | 35% | - Agent behavior configuration
|
| Topic 3: Prompt Engineering | 25% | - Prompt Builder
|
| Topic 4: Data and AI Governance | 20% | - Data grounding
|
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NEW QUESTION # 206
Choose 1 option.
Cloud Kicks wants to integrate its agent with its custom website. The goal is for customers to interact with the custom agent chat interface.
Which approach provides the framework for the custom web application to communicate with the agent?
Answer: A
Explanation:
The AgentForce API Integration Guide defines the Agent API as the framework that enables external web or mobile applications to communicate directly with Salesforce-hosted agents. This API supports message exchange, session management, and context persistence - allowing developers to build custom chat interfaces while maintaining secure, real-time connectivity with the AgentForce reasoning engine.
Option A (A2A) is for inter-agent collaboration within Salesforce, not for external web integration. Option B (MCP) - Model Context Protocol - is used for context sharing between models and tools, not for front- end integration.
Therefore, the correct framework for enabling communication between a custom website chat interface and an AgentForce agent is Option C - Agent API, as it provides the structured interface for external client applications.
Reference: AgentForce Integration Manual - "Using the Agent API for Web and Application-Based Interactions."
NEW QUESTION # 207
A sales rep at Universal Containers is extremely busy and sometimes will have very long sales calls on voice and video calls and might miss key details. They are just starting to adopt new generative AI features.
Which Einstein Generative AI feature should An Agentforce recommend to help the rep get thedetails they might have missed during a conversation?
Answer: A
Explanation:
For a sales rep who may miss key details during long sales calls, theAgentforce Specialistshould recommend theCall Summaryfeature.Call SummaryusesEinstein Generative AIto automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.
* Call Exploreris designed for manually searching through call data but doesn't summarize.
* Sales Summaryis focused more on summarizing overall sales activity, not call-specific content.
For more details, refer toSalesforce's Call Summary documentationon how AI-generated summaries can improve sales rep productivity.
NEW QUESTION # 208
Universal Containers (UC) would like to implement the Sales Development Representative (SDR) Agent.
Which channel consideration should UC be aware of while implementing it?
Answer: B
Explanation:
Universal Containers (UC) is implementing the Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify leads and schedule meetings. Channel considerations are critical for deployment. Let's evaluate the options based on official Salesforce documentation.
Option A: SDR Agent must be deployed in the Messaging channel.The Agentforce SDR Agent is designed to engage prospects in real-time conversations, primarily through the Messaging channel (e.g., Salesforce Messaging for in-app or web chat). This aligns with its purpose of qualifying leads interactively and scheduling meetings, as outlined in Agentforce for Sales documentation. While it may leverage email for follow-ups, its core deployment and interaction occur via Messaging, making this a key consideration UC must be aware of. This is the correct answer.
Option B: SDR Agent only works in the Email channel.The SDR Agent is not limited to email. While it can send emails (e.g., follow-ups after lead qualification), its primary function-real-time lead engagement- relies on Messaging. Stating it "only works in the Email channel" is inaccurate and contradicts its documented capabilities, making this incorrect.
Option C: SDR Agent must also be deployed on the company website.While the SDR Agent can be embedded on a company website via Messaging (e.g., as a chat widget), this is an implementation choice, not a mandatory requirement. The agent's deployment is channel-specific (Messaging), and website integration is optional, not a "must." This option overstates the requirement, making it incorrect.
Why Option A is Correct:
The SDR Agent's primary deployment in the Messaging channel is a documented consideration for its real- time lead qualification capabilities. UC must plan for this channel to ensure effective implementation, as per Salesforce guidelines.
References:
Salesforce Agentforce Documentation: SDR Agent Setup > Channels - Specifies Messaging as the primary channel.
Trailhead: Explore Agentforce Sales Agents - Notes SDR Agent's Messaging focus for lead engagement.
Salesforce Help: Agentforce for Sales > SDR Agent - Confirms Messaging deployment requirement.
NEW QUESTION # 209
Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses.
UC notices that the retriever is returning too many irrelevant results, making the responses less useful. What should UC do to ensure only relevant data is retrieved?
Answer: A
Explanation:
In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses. UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the
'Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.
Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.
Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC's goal of improving relevance.
Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.
Option A is the most effective step to ensure relevance in retrieved data.
Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)
Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)
NEW QUESTION # 210
An Agentforce is considering using a Field Generation prompt template type.
What should the Agentforce Specialist check before creating the Field Generation prompt to ensure it is possible for the field to be enabled for generative AI?
Answer: A
Explanation:
Before creating a Field Generation prompt template in Agentforce, the Specialist must ensure that the target field is available on a Dynamic Form-enabled Lightning page layout. Field Generation prompt templates work by embedding AI-generated suggestions directly into editable record fields on the Lightning record page. This functionality requires Dynamic Forms, which allow fields to be placed and managed as individual components on the Lightning page. Without Dynamic Forms enabled, the AI cannot surface suggestions directly into the field.Generation templates.
NEW QUESTION # 211
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