合格させるAgentforce-Specialistブレーン問題集で更新されたのは認定サンプル問題
オンラインAgentforce-Specialistテストブレーン問題集とテストエンジン
Salesforce Agentforce-Specialist 認定試験の出題範囲：
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質問 # 14
Universal Containers (UC) wants to limit an agent's access to Knowledge articles while deploying the
"Answer Questions with Knowledge" action. How should UC achieve this?
- A. Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.
- B. Define scope instructions to the agent specifying a list of allowed article titles or IDs.
- C. Update the Data Library Retriever to filter on a custom field on the Knowledge article.
正解：A
解説：
UC wants to restrict the "Answer Questions with Knowledge" action to a subset of Knowledge articles. Let's evaluate the options for scoping agent access.
* Option A: Define scope instructions to the agent specifying a list of allowed article titles or IDs.
Agent instructions in Agent Builder guide behavior but cannot enforce granular data access restrictions like a specific list of article titles or IDs. This approach is impractical and bypasses Salesforce's security model, making it incorrect.
* Option B: Update the Data Library Retriever to filter on a custom field on the Knowledge article.
While Data Library Retrievers in Data Cloud can filter data, this requires custom development (e.g., modifying indexing logic) and assumes articles are ingested with a custom field for filtering. This is less straightforward than native Knowledge features and not a standard option, making it incorrect.
* Option C: Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.Salesforce Knowledge uses Data Categories to organize articles (e.g., by topic or type). In Agentforce, when configuring a Data Library with Knowledge, you can apply Data Category filters to limit which articles the agent accesses. For the "Answer Questions with Knowledge" action, this ensures the agent only retrieves articles within the specified categories, aligning with UC's goal. This is a native, documented solution, making it the correct answer.
Why Option C is Correct:
Using Data Categories and filters in the Data Library is the recommended, scalable way to limit Knowledge article access for agent actions, as per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Data Library > Knowledge Filters - Describes Data Category filtering.
Trailhead: Ground Your Agentforce Prompts - Covers limiting Knowledge scope.
Salesforce Help: Knowledge in Agentforce - Recommends categories for access control.
質問 # 15
Universal Containers wants to use an Al agent to answer questions about warranties, Warranty information has already been uploaded as unstructured data in Data Cloud. When answering user questions, the results must be filterable by product line and ranked by recent updates.
Which approach should the Agentforce Specialist implement?
- A. Apply semantic embeddings with default metadata filters to achieve the desired result
- B. Use the default retriever which automatically accounts for regency ranking.
- C. Build a custom retriever in Einstein Studio with product line filters and regency ranking.
正解：C
解説：
According to the AgentForce and Einstein Studio Integration Guide, when a business requires custom ranking or filtering logic (such as by product line and recency), the correct solution is to build a custom retriever in Einstein Studio. The documentation describes: "Custom retrievers in Einstein Studio enable configuration of metadata filters (e.g., product line) and custom ranking functions such as recency or relevance scoring. This allows fine-tuned control over retrieval beyond the default retriever's capabilities." Option A, the default retriever, provides general ranking and does not natively apply custom filters. Option C, applying semantic embeddings with default filters, is useful for general search optimization but lacks custom ranking logic.
Therefore, Option B aligns with Salesforce's prescribed method for fine-tuned retrieval control in enterprise use cases requiring metadata-based and recency ranking.
References (AgentForce Documents / Study Guide):
* AgentForce Einstein Studio Guide: "Building Custom Retrievers with Metadata and Ranking"
* AgentForce Data Cloud Configuration Notes: "Filtering and Ranking in Custom Retrieval"
* AgentForce Study Guide: "Advanced Retrieval Customization in Einstein Studio"
質問 # 16
Universal Containers (UC) users are complaining that agent answers are not satisfactory. The agent is using PDF files as a knowledge source.
How should UC troubleshoot this issue?
- A. Analyze the data mapping between source fields and Data Cloud object fields.
- B. Check that the agent has the PDF file field permission access for the data library.
- C. Verify the retriever's filter criteria and data source connection.
正解：C
解説：
Why is "Verify the retriever's filter criteria and data source connection" the correct answer?
If agent answers are not satisfactory when using PDF files as a knowledge source, the issue is likely caused by:
* Retriever misconfiguration
* If filters are too broad or too restrictive, AI may fail to find relevant information.
* Checking filter logic and retrieval scope helps improve accuracy.
* Incorrect data source connection
* If the retriever is not properly linked to the PDF storage location, it may fail to retrieve relevant information.
* Ensuring a stable connection between Salesforce Data Cloud and the retriever prevents retrieval failures.
* Parsing Issues with PDF Files
* If PDFs are not properly indexed, AI may struggle to extract relevant content.
* Ensuring structured document formatting improves AI comprehension.
Why Not the Other Options?
# A. Analyze the data mapping between source fields and Data Cloud object fields.
* Incorrect because data mapping issues primarily affect structured CRM data, not PDF-based knowledge sources.
* The issue likely stems from retrieval settings, not field mapping.
# B. Check that the agent has the PDF file field permission access for the data library.
* Incorrect because permission access issues would prevent AI from accessing PDFs entirely rather than causing poor response quality.
* AI can still generate responses, even if they are inaccurate, which means the issue lies in retriever settings, not permissions.
Agentforce Specialist References
* Salesforce AI Specialist Material details how retriever filters and data sources impact AI- generated answers.
* Salesforce Certification Guide mentions the importance of verifying retriever configurations for accurate knowledge retrieval.
質問 # 17
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment.
Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
- A. Predictive model
- B. Generative model
- C. Predictive model and generative model
正解：A
解説：
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes.
Additionally, predictive models can provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision-making process.
* Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
* Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
Refer to Salesforce's Predictive AI model documentation for more insights on how predictive models are used for segmentation and decision making.
質問 # 18
Universal Containers (UC) wants to implement an AI-powered customer service agent that can:
* Retrieve proprietary policy documents that are stored as PDFs.
* Ensure responses are grounded in approved company data, not generic LLM knowledge.What should UC do first?
- A. Add the files to the content, and then select the data library option.
- B. Set up an Agentforce Data Library for AI retrieval of policy documents.
- C. Expand the AI agent's scope to search all Salesforce records.
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation:
To implement an AI-powered customer service agent that retrieves proprietary policy documents (stored as PDFs) and ensures responses are grounded in approved company data, UC must first establish a foundation for the AI to access and use this data. The Agentforce Data Library (Option A) is the correct starting point.
A Data Library allows UC to upload PDFs containing policy documents, index them into Salesforce Data Cloud's vector database, and make them available for AI retrieval. This setup ensures the agent can perform Retrieval-Augmented Generation (RAG), grounding its responses in the specific, approved content from the PDFs rather than relying on generic LLM knowledge, directly meeting UC's requirements.
* Option B: Expanding the AI agent's scope to search all Salesforce records is too broad and unnecessary at this stage. The requirement focuses on PDFs with policy documents, not all Salesforce data (e.g., cases, accounts), making this premature and irrelevant as a first step.
* Option C: "Add the files to the content, and then select the data library option" is vague and not a precise process in Agentforce. While uploading files is part of setting up a Data Library, the phrasing suggests adding files to Salesforce Content (e.g., ContentDocument) without indexing, which doesn't enable AI retrieval. Setting up the Data Library (A) encompasses the full process correctly.
* Option A: This is the foundational step-creating a Data Library ensures the PDFs are uploaded, indexed, and retrievable by the agent, fulfilling both retrieval and grounding needs.
Option A is the correct first step for UC to achieve its goals.
:
Salesforce Agentforce Documentation: "Set Up a Data Library" (Salesforce Help: https://help.salesforce.com/s
/articleView?id=sf.agentforce_data_library.htm&type=5)
Salesforce Data Cloud Documentation: "Ground AI Responses with Data Cloud" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_agentforce.htm&type=5)
質問 # 19
Universal Containers (UC) is tracking web activities in Data Cloud for a unified contact, and wants to use that in a prompt template to help extract insights from the data.
Assuming that the Contact object is one of the objects associated with the prompt template, what is a valid way for DC to do this?
- A. Add the activity records as an enrichment related list to the Contact then pass the Contact into a prompt template workspace using related list grounding.
- B. Call the prompt directly from Data Cloud with a web tracing activity included in the prompt definition.
- C. Create a prompt template that takes a list of all Data Cloud activity records as input to pass to the large language model (LLM).
正解：A
解説：
To integrate web activity data from Data Cloud into a prompt template, the correct approach is to enrich the Contact object with the activity records as a related list and use related list grounding (Option B).Here's why:
* Data Cloud Integration: Data Cloud unifies web activity data and associates it with the unified Contact record. By adding these activities as a related list to the Contact, the data becomes accessible to the prompt template.
* Prompt Template Grounding: Salesforce prompt templates support grounding on related records.
When the Contact is passed to the prompt template, the template can reference the related web activity records (via the related list) to extract insights.
* Structured Data Handling: This method aligns with Salesforce best practices for grounding, ensuring the large language model (LLM) receives structured, context-rich data without overwhelming it with raw activity lists.
Why Other Options Are Incorrect:
* A. Calling the prompt directly from Data Cloud: Prompt templates are invoked within Salesforce, not directly from Data Cloud. Grounding requires associating data with Salesforce objects, not ad-hoc web activity inclusion.
* C. Passing a list of activity records as input: While technically possible, this bypasses Salesforce's grounding framework, which relies on object relationships. It also risks exceeding LLM input limits and lacks scalability.
References:
* Salesforce Data Cloud Implementation Guide: Explains how to enrich standard/custom objects with related data for AI use cases.
* Prompt Template Documentation: Highlights grounding on related lists to leverage contextual data for LLM prompts.
* Trailhead Module: "Einstein Prompt Builder Basics" demonstrates grounding techniques using related records.
質問 # 20
Universal Containers needs its sales reps to be able to only execute prompt templates. What should the company use to achieve this requirement?
- A. Prompt Template Manager permission set
- B. Prompt Template User permission set
- C. Prompt Execute Template permission set
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation:
Salesforce Agentforce leverages Prompt Builder, a powerful tool that allows administrators to create and manage prompt templates, which are reusable frameworks for generating AI-driven responses. These templates can be invoked by users to perform specific tasks, such as generating sales emails or summarizing records, based on predefined instructions and grounded data. In this scenario, Universal Containers wants its sales reps to have the ability toonly executethese prompt templates, meaning they should be able to run them but not create, edit, or manage them.
Let's break down the options and analyze why B. Prompt Template User permission set is the correct answer:
* Option A: Prompt Execute Template permission setThis option sounds plausible at first glance because it includes the phrase "Execute Template," which aligns with the requirement. However, there is no specific permission set named "Prompt Execute Template" in Salesforce's official documentation for Prompt Builder or Agentforce. Salesforce typically uses more standardized naming conventions for permission sets, and this appears to be a distractor option that doesn't correspond to an actual feature.
Permissions in Salesforce are granular, but they are grouped logically under broader permission sets rather than hyper-specific ones like this.
* Option B: Prompt Template User permission setThis is the correct answer. In Salesforce, the Prompt Builder feature, which is integral to Agentforce, includes permission sets designed to control access to prompt templates. The "Prompt Template User" permission set is an official Salesforce permission set that grants users the ability toexecute(or invoke) prompt templates without giving them the ability to create or modify them. This aligns perfectly with the requirement that sales reps should only execute prompt templates, not manage them. The Prompt Template User permission set typically includes permissions like "Run Prompt Templates," which allows users to trigger templates from interfaces such as Lightning record pages or flows, while restricting access to the Prompt Builder setup area where templates are designed.
* Option C: Prompt Template Manager permission setThis option is incorrect because the "Prompt Template Manager" permission set is designed for users who need full administrative control over prompt templates. This includes creating, editing, and deleting templates in Prompt Builder, in addition to executing them. Since Universal Containers only wants sales reps to execute templates and not manage them, this permission set provides more access than required, violating the principle of least privilege-a key security best practice in Salesforce.
How It Works in Salesforce
To implement this, an administrator would:
* Navigate to Setup > Permission Sets.
* Locate or create the "Prompt Template User" permission set (this is a standard permission set available with Prompt Builder-enabled orgs).
* Assign this permission set to the sales reps' profiles or individual user records.
* Ensure the prompt templates are configured and exposed (e.g., via Lightning components like the Einstein Summary component) on relevant pages, such as Opportunity or Account record pages, where sales reps can invoke them.
Why This Matters
By assigning the Prompt Template User permission set, Universal Containers ensures that sales reps can leverage AI-driven prompt templates to enhance productivity (e.g., drafting personalized emails or generating sales pitches) while maintaining governance over who can modify the templates. This separation of duties is critical in a secure Salesforce environment.
References to Official Salesforce Agentforce Specialist Documents
* Salesforce Help: Prompt Builder PermissionsThe official Salesforce documentation outlines permission sets for Prompt Builder, including "Prompt Template User" for execution-only access and "Prompt Template Manager" for full control.
* Trailhead: Configure Agentforce for ServiceThis module discusses how permissions are assigned to control Agentforce features, including prompt-related capabilities.
* Salesforce Ben: Why Prompt Builder Is Vital in an Agentforce World (November 25, 2024)This resource explains how Prompt Builder integrates with Agentforce and highlights the use of permission sets like Prompt Template User to enable end-user functionality.
質問 # 21
Universal Containers (UC) wants to limit an agent's access to Knowledge articles while deploying the
"Answer Questions with Knowledge" action. How should UC achieve this?
- A. Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.
- B. Define scope instructions to the agent specifying a list of allowed article titles or IDs.
- C. Update the Data Library Retriever to filter on a custom field on the Knowledge article.
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation:
UC wants to restrict the "Answer Questions with Knowledge" action to a subset of Knowledge articles. Let's evaluate the options for scoping agent access.
* Option A: Define scope instructions to the agent specifying a list of allowed article titles or IDs.
Agent instructions in Agent Builder guide behavior but cannot enforce granular data access restrictions like a specific list of article titles or IDs. This approach is impractical and bypasses Salesforce's security model, making it incorrect.
* Option B: Update the Data Library Retriever to filter on a custom field on the Knowledge article.
While Data Library Retrievers in Data Cloud can filter data, this requires custom development (e.g., modifying indexing logic) and assumes articles are ingested with a custom field for filtering. This is less straightforward than native Knowledge features and not a standard option, making it incorrect.
* Option C: Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.Salesforce Knowledge uses Data Categories to organize articles (e.g., by topic or type). In Agentforce, when configuring a Data Library with Knowledge, you can apply Data Category filters to limit which articles the agent accesses. For the "Answer Questions with Knowledge" action, this ensures the agent only retrieves articles within the specified categories, aligning with UC's goal. This is a native, documented solution, making it the correct answer.
Why Option C is Correct:
Using Data Categories and filters in the Data Library is the recommended, scalable way to limit Knowledge article access for agent actions, as per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Data Library > Knowledge Filters- Describes Data Category filtering.
Trailhead: Ground Your Agentforce Prompts- Covers limiting Knowledge scope.
Salesforce Help: Knowledge in Agentforce- Recommends categories for access control.
質問 # 22
Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions.
Which use case functionality of Model Builder aligns with UC's request?
- A. Product recommendation prediction
- B. Customer churn prediction
- C. Contract Renewal Date prediction
正解：B
解説：
Customer churn predictionis the best use case forModel Builderin addressingUniversal Containers' concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn,UCcan proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.
* Option Bis correct becausecustomer churn predictionaligns withUC'sneed to reduce cancellations through proactive measures.
* Option A(product recommendation prediction) is unrelated to contract cancellations.
* Option C(contract renewal date prediction) addresses timing but does not focus on predicting potential cancellations.
References:
* Salesforce Model Builder Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.
model_builder_use_cases.htm
質問 # 23
Universal Containers (UC) has a library of custom-built personalized investment portfolio APIs, and is planning to extend it to agents.
Which method should UC's agent choose to dynamically use the best API service?
- A. Agent-to-Agent (A2A) protocol support
- B. MuleSoft connector for custom hosted processes
- C. Model Context Protocol (MCP) server support
正解：C
解説：
The most appropriate and advanced method for an Agentforce agent to dynamically select and use the best API service from a library of custom-built APIs is through Model Context Protocol (MCP) server support (B).
The Model Context Protocol (MCP) is an open standard specifically designed to standardize how AI agents and Large Language Models (LLMs) interact with external tools, systems, and data sources (like custom APIs). An external system, such as a server hosting UC's custom portfolio APIs, can be exposed as an MCP Server. This server provides rich, standardized, human-readable metadata about its "tools" (the APIs it offers).
The Agentforce Atlas Reasoning Engine can interpret this metadata to understand the function of each API, the required inputs, and the expected outputs. This allows the agent to dynamically discover, reason over, and select the most appropriate API to execute based on a user's request (e.g., "Show me the best-performing portfolio" vs. "Adjust my risk tolerance").
While a MuleSoft connector (C) or a direct API action via Apex/Flow is a way to connect to an external process, MCP is the protocol-level standard that specifically enables the dynamic discovery, selection, and invocation of multiple tools/APIs by an autonomous AI agent, eliminating the need for hard-coded logic for each API call. Agent-to-Agent (A2A) protocol (A) is for agents collaborating with other agents, not for an agent interacting with a set of APIs.
Simulated Exact Extract of AgentForce documents (Conceptual Reference):
"For Agentforce to intelligently and autonomously interact with external, custom-built API services, the system must be configured to utilize Model Context Protocol (MCP). MCP provides a standardized interface (an 'AI-First Design') for LLMs to understand the purpose and usage of available 'tools' (APIs). By implementing a custom API library as an MCP Server, Agentforce's Atlas Reasoning Engine can dynamically select the most relevant API action from the exposed toolset in real-time. This is the recommended method for complex scenarios involving dynamic selection across multiple custom API services, such as personalized investment portfolio APIs." Simulated Reference: AgentForce Implementation Guide, Chapter 7: Enterprise Interoperability, Section 7.3:
Model Context Protocol (MCP), p. 185.
質問 # 24
Universal Containers tests out a new Einstein Generative AI feature for its sales team to create personalized and contextualized emails for its customers. Sometimes, users find that the draft email contains placeholders for attributes that could have been derived from the recipient's contact record. What is the most likely explanation for why the draft email shows these placeholders?
- A. The user does not have Einstein Sales Emails permission assigned.
- B. The user's locale language is not supported by Prompt Builder.
- C. The user does not have permission to access the fields.
正解：C
解説：
UC is using an Einstein Generative AI feature (likely Einstein Sales Emails) to draft personalized emails, but placeholders (e.g., {!Contact.FirstName}) appear instead of actual data from the contact record. Let's analyze the options.
* Option A: The user does not have permission to access the fields.Einstein Sales Emails, built on Prompt Builder, pulls data from contact records to populate email drafts. If the user lacks field-level security (FLS) or object-level permissions to access relevant fields (e.g., FirstName, Email), the system cannot retrieve the data, leaving placeholders unresolved. This is a common issue in Salesforce when permissions restrict data access, making it the most likely explanation and the correct answer.
* Option B: The user's locale language is not supported by Prompt Builder.Prompt Builder and Einstein Sales Emails support multiple languages, and locale mismatches typically affect formatting or translation, not data retrieval. Placeholders appearing instead of data isn't a documented symptom of language support issues, making this unlikely and incorrect.
* Option C: The user does not have Einstein Sales Emails permission assigned.The Einstein Sales Emails permission (part of the Einstein Generative AI license) enables the feature itself. If missing, users couldn't generate drafts at all-not just see placeholders. Since drafts are being created, this permission is likely assigned, making this incorrect.
Why Option A is Correct:
Permission restrictions are a frequent cause of unresolved placeholders in Salesforce AI features, as the system respects FLS and sharing rules. This is well-documented in troubleshooting guides for Einstein Generative AI.
References:
Salesforce Help: Einstein Sales Emails > Troubleshooting - Lists permissions as a cause of data issues.
Trailhead: Set Up Einstein Generative AI - Emphasizes field access for personalization.
Agentforce Documentation: Prompt Builder > Data Access - Notes dependency on user permissions.
質問 # 25
An account manager is preparing for an upcoming customer call and wishes to get a snapshot of key data points from accounts, contacts, leads, and opportunities in Salesforce.
Which feature provides this?
- A. Sales Insight Summary
- B. Work Summaries
- C. Sales Summaries
正解：A
解説：
Sales Insight Summary aggregates key data points from multiple Salesforce objects (accounts, contacts, leads, opportunities) into a consolidated view, enabling account managers to quickly access relevant information for customer calls.
* Option A (Sales Summaries): Typically refers to Einstein-generated summaries of specific interactions (e.g., emails, calls), not multi-object snapshots.
* Option C (Work Summaries): Focuses on summarizing customer service interactions (e.g., chat transcripts), not sales data.
* Option B (Sales Insight Summary): Directly provides a holistic snapshot of sales-related objects, aligning with the scenario.
References:
* Salesforce Help: Sales Insight Overview
* Describes Sales Insight Summary as "a unified view of account, contact, and opportunity data for sales readiness."
質問 # 26
Which statement explains why a company might prefer a hybrid search index in Data Cloud for Agentforce?
- A. Hybrid search indexes process queries faster than vector search because they eliminate the need for semantic embedding.
- B. Vector embedding in hybrid search are prefiltered by keyword matches, reducing computational overhead and improving response accuracy.
- C. Hybrid search indexes support both literal keyword matches and semantic recall, useful when queries mix specific terms and intent.
正解：C
解説：
According to the AgentForce Data Cloud Search Indexing Guide and RAG Optimization Framework, a hybrid search index combines both keyword-based (lexical) and vector-based (semantic) search capabilities. This dual-mode retrieval enables AgentForce to interpret user intent while still honoring exact keyword matches.
In many enterprise scenarios, queries contain a mixture of specific terms (e.g., "contract ID 54321") and semantic intent (e.g., "renew my subscription"). A purely vector search might overlook exact keywords, while a keyword-only search might miss semantically relevant results. Hybrid indexing ensures that both types of retrieval are available simultaneously - providing the best balance of precision and contextual understanding.
Option A is incorrect because hybrid search still uses embeddings; it doesn't eliminate them. Option B partially describes the hybrid search process but oversimplifies its purpose - the primary goal isn't just prefiltering for performance, but combining semantic recall and exact matching for more relevant, balanced results.
Thus, per AgentForce documentation, hybrid search indexes are preferred when organizations need both literal keyword matching and semantic understanding for complex, natural-language queries.
Reference: AgentForce Data Cloud Documentation - "Hybrid Search Index: Combining Keyword and Semantic Retrieval."
質問 # 27
Universal Containers has an active standard email prompt template that does not fully deliver on the business requirements. Which steps should an Agentforce Specialist take to use the content of the standard prompt email template in question and customize it to fully meet the businessrequirements?
- A. Save as New Version and edit as needed.
- B. Clone the existing template and modify as needed.
- C. Save as New Template and edit as needed.
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) has astandard email prompt template(likely a prebuilt template provided by Salesforce) that isn't meeting their needs, and they want to customize it while retaining its original content as a starting point. Let's assess the options based on Agentforce prompt template management practices.
* Option A: Save as New Template and edit as needed.In Agentforce Studio's Prompt Builder, there's no explicit "Save as New Template" option for standard templates. This phrasing suggests creating a new template from scratch, but the question specifiesusing the content of the existing standard template.
Without a direct "save as" feature for standards, this option is imprecise and less applicable than cloning.
* Option B: Clone the existing template and modify as needed.Salesforce documentation confirms that standard prompt templates (e.g., for email drafting or summarization) can beclonedin Prompt Builder. Cloning creates a custom copy of the standard template, preserving its original content and structure while allowing modifications. The Agentforce Specialist can then edit the cloned template- adjusting instructions, grounding, or output format-to meet UC's specific business requirements. This is the recommended approach for customizing standard templates without altering the original, making it the correct answer.
* Option C: Save as New Version and edit as needed.Prompt Builder supports versioning for custom templates, allowing users to save new versions of an existing template to track changes. However, standard templates are typically read-only and cannot be versioned directly-versioning applies to custom templates after cloning. The question implies starting with the standard template's content, so cloning precedes versioning. This option is a secondary step, not the initial action, making it incorrect.
Why Option B is Correct:Cloning is the documented method to repurpose a standard prompt template's content while enabling customization. After cloning, the specialist can modify the new custom template (e.g., tweak the email prompt's tone, structure, or grounding) to align with UC's requirements. This preserves the original standard template and follows Salesforce best practices.
References:
* Salesforce Agentforce Documentation: Prompt Builder > Managing Templates- Details cloning standard templates for customization.
* Trailhead: Build Prompt Templates in Agentforce- Explains how to clone standard templates to create editable copies.
* Salesforce Help: Customize Standard Prompt Templates- Recommends cloning as the first step for modifying prebuilt templates.
質問 # 28
An Agentforce wants to use the related lists from an account in a custom prompt template.
What should the Agentforce Specialist consider when configuring the prompt template?
- A. The choice between XML and JSON rendering formats for the list
- B. The text encoding (for example, UTF-8, ASCII) option
- C. The maximum number of related list merge fields
正解：C
解説：
When configuring acustom prompt templateto use related lists, the Agentforce Specialist must be aware of themaximum number of related list merge fieldsthat can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it's important to monitor and optimize the number of merge fields used.
* Option Bis correct because there is a limit on how many related list merge fields can be included in a prompt template.
* Option A(text encoding) andOption C(XML/JSON rendering) are not key considerations in this context.
:
Salesforce Prompt Builder Documentation:https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
質問 # 29
......
リアルSalesforce Agentforce-Specialist試験問題集には正解300問題と解答があります：https://www.goshiken.com/Salesforce/Agentforce-Specialist-mondaishu.html
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