Salesforce Certified Agentforce Specialist最新学習資料、AI-201有効試験問題集、Salesforce Certified Agentforce Specialist日本語問題集デモ

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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
AI Agents	35%	- Agent architecture and reasoning engine - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent User security and permissions - Agent types: Service, Sales, Employee - Deterministic controls: filters and variables
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
Development Lifecycle	15%	- Version control and maintenance - Testing agents in Testing Center - Deployment: sandbox to production - Monitoring, adoption and optimization
Prompt Engineering	20%	- Prompt management and user roles - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types - Grounding techniques and best practices
Multi-Agent Interoperability	5%	- Agent API usage scenarios - Model Context Protocol (MCP) - Agent-to-agent communication

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Salesforce Certified Agentforce Specialist 認定 AI-201 試験問題 (Q86-Q91):

質問 # 86
A sales manager needs to contact leads at scale with hyper-relevant solutions and customized communications in the most efficient manner possible. Which Salesforce solution best suits this need?

A. Einstein Sales Assistant
B. Einstein Lead follow-up
C. Prompt Builder

正解：C

解説：
The question specifies a sales manager's need to:
Contact leads at scale: Handle a large volume of leads simultaneously.
Hyper-relevant solutions: Deliver tailored solutions based on lead-specific data (e.g., CRM data, behavior).
Customized communications: Personalize outreach (e.g., emails, messages) for each lead.
Most efficient manner possible: Minimize manual effort and maximize automation.
This suggests a solution that leverages AI for personalization and automation for scale, ideally within the Salesforce ecosystem.

質問 # 87
Universal Containers' administrator has developed a new agent in a sandbox environment and now wants to deploy it to production. What should the administrator do to deploy an agent?

A. Create an outbound change set with all the necessary agent components, then upload to production.
B. Export agent components as JSON files and manually import them inte production using the Metadata API.
C. Manually recreate the agent configuration, topics, and actions in production because change sets cannot be used.

正解：A

解説：
As per the AgentForce Deployment and Lifecycle Management Guide, AgentForce agents, including their topics, actions, and prompt templates, can be deployed from sandbox to production using Salesforce change sets.
Administrators should create an outbound change set in the sandbox environment that includes all relevant components (Agent definition, prompt templates, topic configurations, flows, and permissions) and then upload it to production for validation and deployment. This process ensures consistency, auditability, and proper dependency tracking between environments.

質問 # 88
What is the main benefit of using a Knowledge article in an Agentforce Data Library?

A. It provides a structured, searchable repository of approved documents so the agent can retrieve reliable information for each inquiry..
B. Only the retriever for Knowledge articles allows for agents to access Knowledge from both inside the platform and on a customer's website.
C. The retriever for Knowledge articles has better accuracy and performance than the default retriever.

正解：A

解説：
Why is "A structured, searchable repository of approved documents" the correct answer?
Using a Knowledge Article in an Agentforce Data Library ensures that agents can quickly access reliable and pre-approved information during customer interactions.
Key Benefits of Knowledge Articles in an Agentforce Data Library:
Ensures Information Accuracy and Consistency
Knowledge articles provide approved, well-structured responses, reducing the risk of misinformation.
This ensures customer service consistency across different agents.
Improves Searchability and AI-Grounded Responses
Articles are indexed and retrieved efficiently by AI-powered search engines.
AI-generated responses are grounded in accurate, structured knowledge, improving response quality.
Enhances Customer Support and Agent Productivity
Agents spend less time searching for information and more time resolving customer inquiries.
Einstein AI can suggest the most relevant articles based on conversation context.

質問 # 89
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing autolaunched flow to query the Information from Oracle ERP, which is the system of record for the order fulfillment process. How should an Agentforce Specialist apply the power of conversational AI to this use case?

A. Create a Flex prompt template in Prompt Builder.
B. Configure the Integration Flow Standard Action in Agent Builder.
C. Create a custom Agent action which calls a flow.

正解：C

解説：
Why is "Create a custom Agent action which calls a flow" the correct answer?
In Agentforce, the best way to allow service agents to query order fulfillment status from an external system (Oracle ERP) using natural language is to create a custom Agent action that invokes an existing autolaunched flow.
Key Considerations for This Approach:
Custom Agent Action Triggers the Flow
A custom Agent action is designed to call Salesforce flows, enabling external system integration.
The flow retrieves real-time fulfillment data from Oracle ERP and returns results to the agent.
Enables AI-Powered Query Execution
The Agent can understand natural language and map user utterances to the correct Agent action.
This ensures that agents receive accurate order fulfillment updates quickly.
No Need for Manual Data Entry
Instead of manually searching Oracle ERP, agents can query fulfillment status using AI-powered Agentforce workflows.

質問 # 90
How does Agentforce select the correct action to resolve a user's request?

A. The reasoning engine identifies the agent action to be executed by its name and action input instructions.
B. Each topic contains a list of the matching action's user utterances so that the agent can map the user request to the right topic and action.
C. The large language model (LLM) selects the right topic and action, if they exist. If there are no matches, the LLM attempts to answer the user's request.

正解：C

解説：
In the AgentForce Architecture and Reasoning Engine Overview, Salesforce explains that the large language model (LLM) drives topic and action selection. The documentation states:
"AgentForce uses an LLM to interpret user intent, map it to existing topics, and trigger the appropriate action when available. If no matching topic or action is found, the LLM attempts to generate a direct response using its available context."

質問 # 91
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