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Salesforce Certified Agentforce Specialist Sample Questions (Q163-Q168):

NEW QUESTION # 163
Which use case is best supported by Salesforce Agent's capabilities?

A. Enable data scientists to train predictive AI models with historical CRM data using built-in machine learning capabilities
B. Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.
C. Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.

Answer: C

Explanation:
Salesforce Agent is designed to provide a conversational AI interface that can be utilized by different types of Salesforce users, such as developers, sales agents, and retailers. It acts as an AI-powered assistant that facilitates natural interactions with the system, enabling users to perform tasks and access data easily. This includes tasks like pulling reports, updating records, and generating personalized responses in real time.
Option A is correct because Agent brings a conversational interface that caters to a wide range of users.
Option B and Option C are more focused on developing and training AI models, which are not the primary functions of Agent.
Salesforce Agent Overview: https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm

NEW QUESTION # 164
An Agentforce Specialist is tasked with analyzing Agent interactions, looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the Agentforce Specialist to achieve this?

A. Agent Event Logs dashboard.
B. AI Audit and Feedback Data dashboard.
C. User Utterances dashboard.

Answer: C

Explanation:
The task requires analyzing user inputs, requests, and queries to identify patterns and trends in Agentforce interactions. Let's assess the options based on Agentforce's analytics capabilities.
Option A: Agent Event Logs dashboard.Agent Event Logs capture detailed technical events (e.g., API calls, errors, or system-level actions) related to agent operations. While useful for troubleshooting or monitoring system performance, they are not designed to analyze user inputs or conversational trends. This option does not meet the requirement and is incorrect.
Option B: AI Audit and Feedback Data dashboard.There's no specific "AI Audit and Feedback Data dashboard" in Agentforce documentation. Feedback mechanisms exist (e.g., user feedback on responses), and audit trails may track changes, but no single dashboard combines these for analyzing user queries and trends.
This option appears to be a misnomer and is incorrect.
Option C: User Utterances dashboard.The User Utterances dashboard in Agentforce Analytics is specifically designed to analyze user inputs, requests, and queries. It aggregates and visualizes what users are asking the agent, identifying patterns (e.g., common topics) and trends (e.g., rising query types). Specialists can use this to refine agent instructions or topics, making it the perfect tool for this task. This is the correct answer per Salesforce documentation.
Why Option C is Correct:
The User Utterances dashboard is tailored for conversational analysis, offering insights into user interactions that align with the specialist's goal of identifying patterns and trends. It's a documented feature of Agentforce Analytics for post-deployment optimization.
References:
Salesforce Agentforce Documentation: Agent Analytics > User Utterances Dashboard - Describes its use for analyzing user queries.
Trailhead: Monitor and Optimize Agentforce Agents - Highlights the dashboard's role in trend identification.
Salesforce Help: Agentforce Dashboards - Confirms User Utterances as a key tool for interaction analysis.

NEW QUESTION # 165
An Agentforce turned on Einstein Generative AI in Setup. Now, the Agentforce Specialist would like to create custom prompt templates in Prompt Builder. However, they cannot access Prompt Builder in the Setup menu.
What is causing the problem?

A. The Prompt Template Manager permission set was not assigned correctly.
B. The Prompt Template User permission set was not assigned correctly.
C. The large language model (LLM) was not configured correctly in Data Cloud.

Answer: A

Explanation:
In order to access and create custom prompt templates inPrompt Builder, the Agentforce Specialist must have thePrompt Template Managerpermission set assigned. Without this permission, they will not be able to accessPrompt Builderin the Setup menu, even thoughEinstein Generative AIis enabled.
* Option Bis correct because thePrompt Template Managerpermission set is required to usePrompt Builder.
* Option A(Prompt Template User permission set) is incorrect because this permission allows users to use prompts, but not create or manage them.
* Option C(LLM configuration in Data Cloud) is unrelated to the ability to accessPrompt Builder.
:
Salesforce Prompt Builder Permissions:https://help.salesforce.com/s/articleView?id=sf.
prompt_builder_permissions.htm

NEW QUESTION # 166
Choose 1 option.
When is the Agent-to-Agent (A2A4) protocol an appropriate communication choice?

A. When agents need to access tools
B. When agents need to invoke third-party API
C. When agents need to collaborate

Answer: C

Explanation:
The Agent-to-Agent (A2A) protocol in AgentForce is specifically designed to facilitate collaboration and coordination between multiple agents. It allows distinct agents - such as Service, Sales, or Employee Agents
- to exchange structured messages, delegate tasks, and share context securely within the Salesforce ecosystem.
AgentForce documentation describes A2A as a collaborative communication framework, enabling agents to
"handoff, request, and respond" to each other in complex workflows. This is useful when different agents specialize in separate domains (e.g., one handles billing inquiries, another manages account upgrades).
Option A is incorrect because invoking third-party APIs is handled via Tool Adapters or Action Integrations, not A2A communication. Option B is incorrect because tool access occurs through Tool Invocations, where the agent directly interacts with external services or APIs.
Hence, the correct answer is Option C - When agents need to collaborate, as A2A is purpose-built for inter- agent communication and cooperative task execution.
Reference: AgentForce Architecture Overview - "Agent-to-Agent (A2A) Protocol for Cross-Agent Collaboration."

NEW QUESTION # 167
What is a key benefit of the Agent-to-Agent (A2A) protocol?

A. Allows auto-onboard third-party agents without additional contracts, trust scores, or shared identity controls
B. Provides a standardized framework for cross-vendor agent discovery and communication
C. Provides a standardized runtime engine for internal agent discovery and communication

Answer: B

Explanation:
The Agent-to-Agent (A2A) Protocol Overview describes A2A as a standardized framework for cross- vendor agent discovery and communication. The documentation specifies:
"A2A enables secure, interoperable communication between AI agents across vendors, platforms, and ecosystems, using standardized APIs and schemas for message exchange and capability discovery." This allows AgentForce agents to interact with external AI systems or partner agents while maintaining data governance and identity controls.
Option B is incorrect because auto-onboarding without contracts or trust verification is not supported.
Option C confuses A2A with the internal reasoning runtime used by AgentForce; A2A operates across systems, not within a single platform.
Therefore, Option A correctly defines the key benefit of the Agent-to-Agent protocol.
References (AgentForce Documents / Study Guide):
* AgentForce Architecture Guide: "Understanding the Agent-to-Agent (A2A) Protocol"
* AgentForce Interoperability Handbook: "Cross-Vendor Agent Communication Framework"
* AgentForce Study Guide: "A2A Integration Standards and Benefits"

NEW QUESTION # 168
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