有効なAI Specialist Agentforce-Specialist問題集はあなたの合格を必ず保証します [Q81-Q98]

有効なAI Specialist Agentforce-Specialist問題集はあなたの合格を必ず保証します

Agentforce-Specialist問題集でリアル試験問題でテストエンジン問題集でトレーニング

Salesforce Agentforce-Specialist 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Agentforce and Service Cloud: This section measures the skills of AI Engineers and focuses on building agents that answer questions based on Knowledge articles and connecting them to digital channels. It also covers identifying the correct generative AI features in Agentforce for Service Cloud scenarios.
トピック 2	Agentforce and Sales Cloud: This section assesses the skills of AI Developers and covers identifying the correct generative AI features in Agentforce for Sales Cloud scenarios. It also includes determining when to use Agentforce Sales Agents, such as Sales Development Representatives (SDRs) and Sales Coaches.
トピック 3	Agentforce and Data Cloud: This section measures the skills of AI Developers and addresses how Agentforce integrates with Data Cloud to improve response accuracy and personalize answers. It involves grounding with retrievers in Data Cloud to enhance agent performance.
トピック 4	Prompt Engineering: This section measures the skills of AI Developers and focuses on prompt engineering techniques. It covers identifying when to use Prompt Builder, managing prompt templates, selecting appropriate grounding techniques, and explaining the process for creating and executing prompt templates.
トピック 5	Agentforce Concepts: This section assesses the skills of AI Engineers and covers how Agentforce works, including its reasoning engine, standard and custom topics, agent actions, and user security management. It also includes testing and deploying agents from sandbox to production environments.

質問 # 81
An Agentforce needs to create a Sales Email with a custom prompt template. They need to ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should theAgentforce Specialistobtain related items?

A. Call prompt initiated flow to fetch and ground the required data.
B. Utilize a standard email template and manually insert the required data fields.
C. Create a flex template that takes the records in question as inputs.

正解：A

解説：
To ground a sales email onOpportunity Products, Events near the customer, and Tone and voice examples, theAgentforce Specialistshould use aprompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.
* Option B (flex template)does not provide the ability to fetch dynamic data from Salesforce records automatically.
* Option C (manual insertion)would not allow for the dynamic and automated grounding of data required for custom prompts.
Refer toSalesforce documentation on flowsand grounding for more details on integrating data into custom prompt templates.

質問 # 82
What should An Agentforce consider when using related list merge fields in a prompt template associated with an Account object in Prompt Builder?

A. Prompt generation will yield no response when there is no related list associated with an Account in runtime.
B. If person accounts have been enabled, merge fields will not be available for the Account object.
C. The Activities related list on the Account object is not supported because it is a polymorphic field.

正解：C

解説：
When using related list merge fields in a prompt template associated with the Account object inPrompt Builder, theActivities related listis not supported due to it being apolymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.
* Option Bis incorrect because person accounts do not limit the availability of merge fields for the Account object.
* Option Cis 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 toSalesforce documentationon supported fields and limitations inPrompt Builder.

質問 # 83
The Agentforce Specialist for Coral Cloud Resorts wants to create an agent that will automate the resolution of a large portion of guest complaints related to their vacation experiences. The agent will be able to offer upgrades, hotel credit, and other complimentary options. The agent will also be in charge of escalating the case to a human when a guest has suffered a major disruption (such as cancellation).
Following Salesforce best practices, which type of agent should the Agentforce Specialist create?

A. Sales A Agent with a Flex prompt template
B. Custom Agent with a Flex prompt template
C. Service Agent with a Flex prompt template

正解：C

解説：
The AgentForce for Service Implementation Guide confirms that when automating customer service and complaint resolution, the correct solution is a Service Agent. The documentation states:
"Service Agents handle customer inquiries, complaints, and issue resolution workflows. They can automate actions such as offering credits, applying upgrades, and escalating severe cases to human support." Flex prompt templates are recommended for these scenarios, as they allow contextual control and personalization based on the complaint details.
Option A (Sales Agent) focuses on sales-related tasks like lead nurturing.
Option B (Custom Agent) could work but lacks the pre-built integrations and actions designed for service workflows.
Thus, Option C aligns with Salesforce's best-practice model for customer issue automation.
References (AgentForce Documents / Study Guide):
AgentForce for Service Guide: "Automating Complaint Resolution"
AgentForce Prompt Template Handbook: "Using Flex Templates in Service Workflows" AgentForce Study Guide: "Deploying Service Agents for Escalation and Resolution Scenarios"

質問 # 84
Choose 1 option.
Coral Cloud Resorts (CCR) sees the agent forgot the dietary/activity preferences gathered earlier. They need those preferences to persist throughout the session.
What should CCR implement?

A. Rely on natural conversation memory and instruct the agent to look back.
B. Configure custom variables to capture/store customer preferences from action outputs.
C. Create a context variable to capture/store customer preferences as action outputs.

正解：C

解説：
According to the AgentForce Session Memory and Context Management Guide, when specific customer preferences (such as dietary or activity selections) must persist throughout an interaction, the correct approach is to use a context variable. The documentation states: "Context variables retain information across the user session, enabling the agent to reference prior inputs or outputs without re-asking. They are ideal for persisting customer preferences, authentication data, or ongoing session parameters." By contrast, custom variables (Option A) are typically used for storing intermediate action outputs but are not automatically persistent across the full session. Relying on conversation memory (Option B) alone is non- deterministic and may cause data loss due to memory truncation or token limits.
Thus, Option C - creating a context variable to store and recall customer preferences - aligns with Salesforce's recommended implementation for session-level persistence.
References (AgentForce Documents / Study Guide):
AgentForce Configuration Guide: "Using Context Variables for Session Data" AgentForce Study Guide: "Persistent Memory and Variable Management" AgentForce Implementation Handbook: "Maintaining Context Across User Sessions"

質問 # 85
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?

A. Increase the maximum number of results returned to capture a broader dataset.
B. Define filters to narrow the search results based on specific conditions.
C. Change the search index to a different data model object (DMO).

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation:
In Salesforce Agentforce, acustom retrieveris 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 todefine 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)

質問 # 86
When creating a custom retriever in Einstein Studio, which step is considered essential?

A. Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.
B. Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.
C. Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.

正解：A

解説：
In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:
Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever's output, but it's a secondary step. The retriever must first know where to search (A) before output can be configured.
Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce Help: https://help.
salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5) Trailhead: "Einstein Studio for Agentforce" (https://trailhead.salesforce.com/content/learn/modules/einstein- studio-for-agentforce)

質問 # 87
Choose 1 option.
Universal Containers has PDF maintenance guides in an external folder, not yet in Salesforce. The team wants a standard, clicks- only setup for the Service Agent to use these documents.
Which approach should the Agentforce Specialist implement?

A. Configure Data Cloud to ingest file attachments and create custom index and retriever for product record and attachment data.
B. Upload the PDFs as File source in the Agentforce Data Library which will build a Search Index, and create a retriever to ground responses from those documents.
C. Paste external PDF links into topic instructions and rely on the model to follow them, avoiding configuration of a retrieval source, index, or retriever action.

正解：B

解説：
According to the AgentForce Data Library and Retrieval Configuration Guide, when organizations have external PDF or text documents that need to be used by an AI agent, the recommended clicks-only approach is to upload the documents as a File Source in the AgentForce Data Library. The system automatically processes the uploaded files, chunks their content, builds a Search Index, and allows you to create a retriever to ground agent responses from those indexed documents.
This method requires no code or manual integration and ensures that all document content becomes queryable through retrieval-augmented generation (RAG).
Option A is incorrect because LLMs cannot reliably access external links; they require indexed data. Option C, involving Data Cloud ingestion, is a more advanced, developer-level approach-not a clicks-only setup.
Hence, the correct implementation is Option B - Upload PDFs as File Source in the AgentForce Data Library and create a retriever for grounding.
Reference: AgentForce Data Library Setup Guide - "Uploading File Sources and Creating Search Indexes."

質問 # 88
An Agentforce configured Data Masking within the Einstein Trust Layer.
How should the Agentforce Specialist begin validating that the correct fields are being masked?

A. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
B. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
C. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.

正解：A

解説：
To begin validating that the correct fields are being masked in Einstein Trust Layer, the Agentforce Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu.
This audit data allows the Agentforce Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.
Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.
Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm

質問 # 89
Universal Containers (UC) wants to enable its sales team with automatic post-call visibility into mention of competitors, products, and other custom phrases.
Which feature should theAgentforce Specialistset up to enable UC's sales team?

A. Call Summaries
B. Call Explorer
C. Call Insights

正解：C

解説：
To enable Universal Containers' sales team with automatic post-call visibility into mentions ofcompetitors, products, and custom phrases, theAgentforce Specialistshould set upCall Insights.Call Insightsanalyzes voice and video calls for key phrases, topics, and mentions, providing insights into critical aspects of the conversation. This feature automatically surfaces key details such as competitor mentions, product discussions, and custom phrases specified by the sales team.
* Call Summariesprovide a general overview of the call but do not specifically highlight keywords or topics.
* Call Exploreris a tool for navigating through call data but does not focus on automatic insights.
For more information, refer toSalesforce's Call Insights documentationregarding the analysis of call content and extracting actionable information.

質問 # 90
Choose 1 option.
An Agentforce Specialist needs to create a prompt template that extracts the customer's name, phone number, and case number from a block of text, and nothing else.
How should the Agentforce Specialist structure the prompt to ensure the large language model (LLM) doesn't include extra conversation or text?

A. Use well-defined output instructions and provide desired output examples.
B. Ensure in the prompt that the LLM has been told to only use name value pairs in the response.
C. Ask the LLM to extract and only output the important information in the text.

正解：A

解説：
According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well- defined output instructions and examples. The documentation specifies: "To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure." AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: "Always define your output schema clearly - for example, specify JSON or key- value pairs - and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language." Option A ("Ask the LLM to extract and only output important information") is too vague and can still produce variable or verbose responses. Option C ("Ensure the LLM has been told to only use name value pairs") is partially correct but incomplete without clear formatting and example output. Therefore, Option B is the correct choice as it aligns with AgentForce's documented standards for prompt accuracy and reliability.
References (AgentForce Documents / Study Guide):
* AgentForce Prompt Engineering Best Practices Guide
* AgentForce Developer Study Guide: "Defining Structured Outputs in Prompt Templates"
* AgentForce Technical Documentation: "Using Output Instructions and Examples for LLM Control"

質問 # 91
Choose 1 option.
What does it mean when a prompt template version is described as immutable?

A. After a prompt template version is activated, no further changes can be saved to that version.
B. Only the latest version of a template can be activated.
C. Every modification on a template will be saved as a new version automatically.

正解：A

解説：
According to the AgentForce Prompt Template Versioning Guide, when a prompt template version is marked as immutable, it means that no edits or modifications can be made to that version after it has been activated. This ensures that the logic, wording, and grounding parameters tied to that version remain locked and consistent for auditing, reproducibility, and compliance purposes.
If further changes are required, the system automatically creates a new draft version of the template. The old version remains immutable and preserved for traceability.
Option B is incorrect because multiple versions can exist, and older versions can remain active under specific testing or rollback scenarios. Option C is partially true but incomplete-the immutability refers to the frozen nature of an active version, not just version creation.
Thus, the correct explanation is Option A - Once a prompt template version is activated, it becomes immutable and cannot be changed.
Reference: AgentForce Prompt Management Documentation - "Immutable Version Control in Prompt Templates."

質問 # 92
An Agentforce Agent has been developed with multiple topics and Agent Actions that use flows and Apex.
Which options are available for deploying these to production?

A. Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI
/Metadata API.
B. Deploy the flows and Apex using normal deployment tools and manually create the agent-related items in production.
C. Use only change sets because the Salesforce CLI does not currently support the deployment of agent- related metadata.

正解：A

解説：
Why is "Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI
/Metadata API" the correct answer?
When deploying an Agentforce Agent with multiple topics and Agent Actions that use flows and Apex, a complete deployment solution is required. Change sets and the Salesforce CLI/Metadata API support the deployment of flows, Apex code, and agent-related metadata.
Key Considerations for Agentforce Deployments:
* Supports Deployment of All Required Components
* Agentforce Agents include flows, Apex classes, topics, and agent actions.
* Change sets and Salesforce CLI/Metadata API allow deployment of all these components together, ensuring a smooth transition to production.
* Agentforce Metadata Can Be Deployed Using Standard Tools
* Change Sets: Allows admins to move configurations, custom objects, and metadata between Salesforce environments.
* Salesforce CLI/Metadata API: Enables scripted deployments, automating the transfer of Agentforce configurations.
* Ensures a Complete Migration Without Manual Configuration
* Deploying all components together reduces the risk of misconfiguration.
* Automating deployments using the Metadata API ensures consistency across environments.
Why Not the Other Options?
# A. Deploy the flows and Apex using normal deployment tools and manually create the agent-related items in production.
* Incorrect because manually creating agent-related items in production introduces risk and inconsistency.
* This approach is error-prone and time-consuming, especially for large Agentforce deployments.
# B. Use only change sets because the Salesforce CLI does not currently support the deployment of agent-related metadata.
* Incorrect because Salesforce CLI and Metadata API fully support Agentforce deployments.
* Change sets are useful but limited in large-scale, automated deployments.
Agentforce Specialist References
* Salesforce AI Specialist Material confirms that Agentforce metadata (flows, actions, and topics) can be deployed using Change Sets or the Metadata API.

質問 # 93
Choose 1 option.
What is an Agentforce Specialist able to do when the 'Enrich event logs with conversation data' setting in the Agentforce configuration is enabled?

A. View the user click path that led to each agent action.
B. View session data including user input and agent responses for sessions.
C. Generate details reports on all agent conversations over any time period.

正解：B

解説：
The AgentForce Event and Logging Configuration Guide states that enabling "Enrich event logs with conversation data" allows administrators to capture session-level details, including both user inputs and agent responses. The documentation explains: "When this setting is enabled, conversation transcripts, user messages, and agent responses are appended to the event logs for improved visibility and troubleshooting." This provides a comprehensive record for analytics, training, and quality review. It does not, however, track user click paths (Option A) or generate aggregated historical reports across all time periods automatically (Option C).
Therefore, Option B is correct, as it directly reflects the documented functionality of the conversation data enrichment feature within AgentForce configuration.
References (AgentForce Documents / Study Guide):
* AgentForce Configuration and Monitoring Guide: "Enrich Event Logs with Conversation Data"
* AgentForce Data and Analytics Study Notes
* AgentForce Implementation Handbook: "Session and Conversation Log Management"

質問 # 94
Universal Container's internal auditing team asks An Agentforce to verify that address information is properly masked in the prompt being generated.
How should the Agentforce Specialist verify the privacy of the masked data in the Einstein Trust Layer?

A. Review the platform event logs
B. Enable data encryption on the address field
C. Inspect the AI audit trail

正解：C

解説：
TheAI audit trailin Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the Agentforce Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
* Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
* Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
* Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is processed and masked in AI activities.
Reference:
"How Salesforce Ensures Trust in AI with Einstein Trust Layer | Salesforce" .

質問 # 95
Universal Containers built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors. What is the cause of the random nature of this error?

A. The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
B. The number of tokens generated by the dynamic nature of the prompt template will vary by record.
C. The number of tokens that can be processed by the LLM varies with total user demand.

正解：B

解説：
In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM's token capacity (e.g., input + output tokens). The random nature of these failures suggests variability in the token count across different records, which is directly addressed by Option B.
Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record-some records might have short text fields while others have lengthy ones-the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM's limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.
* Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation does not define "Flex" as a specific template type for handling token variability in this context (there are Flow-based templates, but they're unrelated to token limits). This option is a distractor and not a verified solution.
* Option C: The LLM's token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens for advanced models) and does not vary with user demand. Demand might affect performance or availability, but not the token limit itself.
Option B is the correct answer because it accurately identifies the dynamic nature of the prompt template as the root cause of variable token counts leading to random failures.
:
Salesforce Agentforce Documentation: "Prompt Templates" (Salesforce Help: https://help.salesforce.com/s
/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
Trailhead: "Build Prompt Templates for Agentforce" (https://trailhead.salesforce.com/content/learn/modules
/build-prompt-templates-for-agentforce)

質問 # 96
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 Summaries
B. Work Summaries
C. Sales Insight Summary

正解：C

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 # 97
An Agentforce Service Agent, who has been successfully assisting customers with service requests in Salesforce, is now unable to help customers with issues related to a new product replacement process. The company recently implemented a custom Product Replacement object in Salesforce to track and manage these replacements. Which Agentforce Agent User change must be implemented to address this issue?

A. The permission set assigned to the Agent User needs Read access to the custom Product Replacement object.
B. The profile assigned to the Agentforce Agent User needs AI training permission to the custom Product Replacement object.
C. The permission set group assigned to the Agent User needs to grant access to the Product Replacement flow.

正解：A

解説：
Why is "Permission Set Read Access" the correct answer?
If an Agentforce Service Agent is unable to assist customers with the new Product Replacement process, it is likely due to missing object permissions.
Key Considerations for Object Access in Agentforce:
Custom Objects Require Permission Set Access
The new Product Replacement object must be explicitly assigned to the agent's permission set.
Without Read access, the agent cannot view or interact with the object.
Ensuring Full Data Access for Agents
In Setup # Permission Sets, the admin should:# Grant Read access to the Product Replacement object# Ensure that related fields (e.g., status, replacement reason) are also accessible Aligning AI and Agent Workflows If Einstein AI is used to suggest solutions, the agent must have visibility into the Product Replacement object for context-aware responses.
Why Not the Other Options?
# A. The permission set group assigned to the Agent User needs to grant access to the Product Replacement flow.
Incorrect because flow permissions only control automation access, not direct object access.
If an agent cannot view the object, the flow will not be visible or usable.
# C. The profile assigned to the Agentforce Agent User needs AI training permission to the custom Product Replacement object.
Incorrect because AI training permissions relate to model learning and improvement, not object visibility.
Agentforce Specialist References
Salesforce AI Specialist Material confirms that permission sets control object-level access for Agentforce users.