Agentforce-Specialist対策学習 & Agentforce-Specialist PDF問題サンプル

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Salesforce Agentforce-Specialist Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Agentforce and Data Cloud20%- Data Library
  • 1. Improve agent's response accuracy and personalize answers with Agentforce Data Library
- Retrievers
  • 1. Ground with retrievers in Data Cloud
Topic 2: Agentforce Concepts30%- Testing and Deployment
  • 1. Deploy an agent from sandbox to production
  • 2. Test an agent using Testing Center
- Management and Monitoring
  • 1. Manage Agentforce user security
  • 2. Manage and monitor agent adoption
- Agent Architecture
  • 1. Explain how an agent works and how the reasoning engine powers Agentforce
- Topics and Actions
  • 1. Leverage standard topics, custom topics, standard agent actions, and custom agent actions
Topic 3: Agentforce and Sales Cloud10%- Sales Cloud Features
  • 1. Identify the correct generative AI feature in Agentforce for Sales
Topic 4: Agentforce and Service Cloud10%- Channel Connection
  • 1. Connect an agent to a digital channel
- Generative AI Features
  • 1. Given a scenario, identify the correct generative AI feature in Agentforce for Service
- Knowledge Integration
  • 1. Build an agent that answers questions based on Knowledge articles
Topic 5: Prompt Engineering30%- Prompt Builder Appropriateness
  • 1. Given business requirements, identify when it's appropriate to use Prompt Builder
- Grounding Techniques
  • 1. Given a scenario, identify the appropriate grounding technique
- Template Creation
  • 1. Explain the process for creating, activating, and executing prompt templates
  • 2. Identify the considerations for creating a prompt template
- User Roles and Management
  • 1. Identify the right user roles to manage and execute prompt templates

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Salesforce Certified Agentforce Specialist (AI-201) 認定 Agentforce-Specialist 試験問題 (Q37-Q42):

質問 # 37
Amid their busy schedules, sales reps at Universal Containers dedicate time to follow up with prospects and existing clients via email regarding renewals or new deals. They spend many hours throughout the week reviewing past communications and details about their customers before performing their outreach. Which standard Agent action helps sales reps draft personalized emails to prospects by generating text based on previous successful communications?

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation:UC's sales reps need an AI action to draft personalized emails based on past successful communications, reducing manual review time. Let's evaluate the standard Agent actions.
* Option A: Agent Action: Summarize Record"Summarize Record" generates a summary of a record (e.g., Opportunity, Contact), useful for overviews but not for drafting emails or leveraging past communications. This doesn't meet the requirement, making it incorrect.
* Option B: Agent Action: Find Similar Opportunities"Find Similar Opportunities" identifies past deals to inform strategy, not to draft emails. It provides data, not text generation, making it incorrect.
* Option C: Agent Action: Draft or Revise Sales EmailThe "Draft or Revise Sales Email" action in Agentforce for Sales (sometimes styled as "Draft Sales Email") uses the Atlas Reasoning Engine to generate personalized email content. It can analyze past successful communications (e.g., via Opportunity or Contact history) to tailor emails for renewals or deals, saving reps time. This directly addresses UC's need, making it the correct answer.
Why Option C is Correct:"Draft or Revise Sales Email" is a standard action designed for personalized email generation based on historical data, aligning with UC's productivity goal per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Agentforce for Sales > Draft Sales Email- Details email generation.
* Trailhead: Explore Agentforce Sales Agents- Covers email drafting with past data.
* Salesforce Help: Sales Features in Agentforce- Confirms personalization capabilities.


質問 # 38
Universal Containers (UC) wants to build an Agentforce Service Agent that provides the latest, active, and relevant policy and compliance information to customers. The agent must:
* Semantically search HR policies, compliance guidelines, and company procedures.
* Ensure responses are grounded on published Knowledge.
* Allow Knowledge updates to be reflected immediately without manual reconfiguration.What should UC do to ensure the agent retrieves the right information?

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
UC requires an Agentforce Service Agent to deliver accurate, up-to-date policy and compliance info with specific criteria. Let's evaluate.
* Option A: Enable the agent to search all internal records and past customer inquiries.Searching all records and inquiries risks irrelevant or outdated responses, conflicting with the need for published Knowledge grounding and immediate updates. This lacks specificity, making it incorrect.
* Option B: Set up an Agentforce Data Library to store and index policy documents for AI retrieval.The Agentforce Data Library integrates with Salesforce Knowledge, indexing HR policies, compliance guidelines, and procedures for semantic search. It ensures grounding in published Knowledge articles, and updates (e.g., new article versions) are reflected instantly without reconfiguration, as the library syncs with Knowledge automatically. This meets all UC requirements, making it the correct answer.
* Option C: Manually add policy responses into the AI model to prevent hallucinations.Manually embedding responses into the model isn't feasible-Agentforce uses pretrained LLMs, not custom training. It also doesn't support real-time updates, making this incorrect.
Why Option B is Correct:
The Data Library meets all criteria-semantic search, Knowledge grounding, and instant updates-per Salesforce's recommended approach.
References:
Salesforce Agentforce Documentation: Data Library > Knowledge Integration- Details indexing and updates.
Trailhead: Build Agents with Agentforce- Covers Data Library for accurate responses.
Salesforce Help: Grounding with Knowledge- Confirms real-time sync.


質問 # 39
Universal Containers (UC) is using related list merge fields in a prompt template associated with an Account object in Prompt Builder.
What should UC consider?

正解：A

解説：
When using related list merge fields in a prompt template associated with the Account object in Prompt Builder, the Activities related list is not supported due to it being a polymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.
Option B is incorrect because person accounts do not limit the availability of merge fields for the Account object.
Option C is irrelevant since even if no related lists are available at runtime, the prompt can still generate based on other available data fields.
For more information, refer to Salesforce documentation on supported fields and limitations in Prompt Builder.


質問 # 40
A data scientist needs to view and manage models in Einstein Studio, and also needs to create prompt templates in Prompt Builder. Which permission sets should an Agentforce Specialist assign to the data scientist?

正解：C

解説：
The data scientist requires permissions for Einstein Studio (model management) and Prompt Builder (template creation). Note: " Einstein Studio " may be a misnomer for Data Cloud's model management or a related tool, but we'll interpret based on context. Let's evaluate.
Option A: Prompt Template Manager and Prompt Template UserThere's no distinct " Prompt Template Manager " or " Prompt Template User " permission set in Salesforce-Prompt Builder access is typically via " Einstein Generative AI User " or similar. This option lacks coverage for Einstein Studio/Data Cloud, making it incorrect.
Option B: Data Cloud Admin and Prompt Template ManagerThe " Data Cloud Admin " permission set grants access to manage models in Data Cloud (assumed as Einstein Studio's context), including viewing and editing AI models. " Prompt Template Manager " isn't a real set, but Prompt Builder creation is covered by " Einstein Generative AI Admin " or similar admin-level access (assumed intent). This combination approximates the needs, making it the closest correct answer despite naming ambiguity.
Option C: Prompt Template User and Data Cloud Admin " Prompt Template User " isn't a standard set, and user-level access (e.g., Einstein Generative AI User) typically allows execution, not creation. The data scientist needs to create templates, so this lacks sufficient Prompt Builder rights, making it incorrect.
Why Option B is Correct (with Caveat):
" Data Cloud Admin " covers model management in Data Cloud (likely intended as Einstein Studio), and " Prompt Template Manager " is interpreted as admin-level Prompt Builder access (e.g., Einstein Generative AI Admin). Despite naming inconsistencies, this fits the requirements per Salesforce permissions structure.
References:
Salesforce Data Cloud Documentation: Permissions - Details Data Cloud Admin for models.
Trailhead: Set Up Einstein Generative AI - Covers Prompt Builder admin access.
Salesforce Help: Agentforce Permission Sets - Aligns with admin-level needs.


質問 # 41
Universal Containers (UC) uses an agent to handle customer service inquiries. UC recently partnered with an external logistics provider that operates its own distinct, autonomous AI agent. When a customer requests a complex international shipping reroute, the UC agent needs to securely communicate, negotiate routing terms, and delegate the execution of the reroute directly to the logistics provider's AI agent.
Which open standard multi-agent protocol is specifically designed to facilitate this autonomous task delegation and negotiation between independent AI agents?

正解：A

解説：
The correct answer is A. Agent-to-Agent (A2A) Protocol is the correct open standard when independent AI agents need to discover each other, communicate securely, exchange structured information, and delegate work across platforms. In this scenario, UC's agent is not merely retrieving data from an external system; it must communicate with another autonomous logistics agent and delegate execution of a shipping reroute. MCP is used to expose external tools, data, and context to agents, but it is not the primary protocol for agent-to-agent negotiation. OpenAPI describes REST APIs and is useful for traditional service integration, but it does not provide an agent collaboration protocol.
Salesforce describes A2A as an open standard for secure communication, task delegation, and collaboration between agents from different platforms.


質問 # 42
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