Salesforce Agentforce-Specialist Certification All-in-One Exam Guide Apr-2026
Get Real Agentforce-Specialist Exam Dumps [Apr-2026] Practice Tests
NEW QUESTION # 20
An Agentforce at Universal Containers is trying to set up a new Field Generation prompt template. They take the following steps.
1. Create a new Field Generation prompt template.
2. Choose Case as the object type.
3. Select the custom field AI_Analysis_c as the target field.
After creating the prompt template, the Agentforce Specialist saves, tests, and activates it. Howsoever, when they go to a case record, the AI Analysis field does not show the (Sparkle) icon on the Edit pencil. When the Agentforce Specialist was editing the field, it was behaving as a normal field.
Which critical step did the Agentforce Specialist miss?
- A. They forgot to reactivate the Lightning page layout for the Case object after activating their Field Generation prompt template.
- B. They forgot to edit the Lightning page layout and associate the field to a prompt template
- C. They forgot that the Case Object is not supported for Add generation as Feinstein Service Replies should be used instead.
Answer: B
Explanation:
For Field Generation prompt templates to display the Sparkle icon (indicating AI-generated content), the target field must be explicitly associated with the prompt template on the Lightning page layout. Even if the prompt template is activated, failing to add the field to the page layout and link it to the template will result in the field behaving as a standard field. Salesforce documentation emphasizes that page layout configuration is mandatory to enable AI-driven field interactions.
Reactivating the layout (A) is unnecessary unless the layout itself was modified after activation.
Case objects are supported for Field Generation (B is incorrect).
Reference:
Salesforce Help Article: Configure Field Generation Prompt Templates ("Associating Fields with Page Layouts" section).
Einstein GPT Implementation Guide: "Enabling AI-Generated Fields in Lightning Pages."
NEW QUESTION # 21
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. Ask the LLM to extract and only output the important information in the text.
- C. Ensure in the prompt that the LLM has been told to only use name value pairs in the response.
Answer: A
Explanation:
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"
NEW QUESTION # 22
An Agentforce wants to ground a new prompt template with the User related list.
What should theAgentforce Specialistconsider?
- A. The User related list should have View All access.
- B. The User related list is not supported in prompt templates.
- C. The User related list needs to be included on the record page.
Answer: B
Explanation:
Salesforce has restrictions on which objects and related lists can be used for grounding prompt templates. This is likely due to security and privacy concerns related to user data.
While it might seem intuitive to use the User related list to provide context to the LLM, Salesforceprevents this to ensure that sensitive user information is not inadvertently exposed or misused.
Therefore, theAgentforce Specialistneeds to explore alternative ways to incorporate the necessary user information into the prompt template, perhaps by using other related objects or fields that are supported.
NEW QUESTION # 23
A service manager wants to use Salesforce Prompt Builder to help agents summarize customer case notes after a support call.
The summary should:
* Capture the customer's issue, troubleshooting steps taken, and next actions.
* Be no longer than five sentences.
* Use plain language (no technical jargon).
If no next action is identified, the summary should explicitly state "No next action required." Which prompt template fallows Salesforce prompt design best practices?
required."
Format: Use numbered sentences for clarity.
- A. Role: You are a case documentation assistant,
Task: Write a summary of the support call.
Context: Always describe the customer issue, troubleshooting, and resolution details.
Constraints: The summary should be comprehensive and professional, but there is no limit on length or language style.
Format: Use complete sentences in a narrative style. - B. Role: You are a support agent writing a case summary.
Task: Provide a professional summary of the issue and troubleshooting steps.
Contest: Include customer issue, steps taken, and next actions if available.
Constraints: No strict sentence limit, but use plain language. If no next action is found, leave it out.
Format: Use paragraphs for readability. - C. Role: You are an experienced support agent.
Task: Summarize the case notes,
Context: Include customer issue, troubleshooting steps, and next actions.
Constraints: Limit to 5 sentences, use plain language, and if no next action is found, state "No next action
Answer: C
Explanation:
According to the Salesforce Prompt Builder Best Practices Guide, an effective prompt must include Role, Task, Context, Constraints, and Format clearly defined - a structure known as the RTCCF model. The documentation explains: "Prompts should specify the assistant's role, define a clear task, include context and constraints, and provide output format instructions to ensure predictable and high-quality responses." Option A follows this framework precisely. It defines:
* Role: The assistant's identity ("experienced support agent").
* Task: Summarizing case notes.
* Context: Customer issue, troubleshooting steps, next actions.
* Constraints: Limit of 5 sentences, plain language, include "No next action required" if applicable.
* Format: Numbered sentences for clarity.
Options B and C omit critical prompt design elements such as strict constraints or output formatting and therefore do not align with Salesforce's prompt design standard.
References (AgentForce Documents / Study Guide):
* Salesforce Prompt Builder Guide: "Prompt Structure Using RTCCF Model"
* AgentForce Prompt Template Design Guide: "Best Practices for Summarization Prompts"
* Salesforce AI Prompt Engineering Study Guide
NEW QUESTION # 24
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.
Answer: A
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.
NEW QUESTION # 25
Universal Containers (UC) wants to enable its sales team to use AI to suggest recommended products from its catalog. Which type of prompt template should UC use?
- A. Email generation prompt template
- B. Record summary prompt template
- C. Flex prompt template
Answer: C
Explanation:
UC needs an AI solution to suggest products from a catalog for its sales team. Let's assess the prompt template types in Prompt Builder.
* Option A: Record summary prompt templateRecord summary templates generate concise summaries of records (e.g., Case, Opportunity). They're not designed for product recommendations, which require dynamic logic beyond summarization, making this incorrect.
* Option B: Email generation prompt templateEmail generation templates craft emails (e.g., customer outreach). While they could mention products, they're not optimized for standalone recommendations, making this incorrect.
* Option C: Flex prompt templateFlex prompt templates are versatile, allowing custom inputs (e.g., catalog data from objects or Data Cloud) and instructions (e.g., "Suggest products based on customer preferences"). This flexibility suits UC's need to recommend products dynamically, making it the correct answer.
Why Option C is Correct:
Flex templates offer the customization needed to suggest products from a catalog, aligning with Salesforce's guidance for tailored AI outputs.
References:
Salesforce Agentforce Documentation: Prompt Builder > Flex Templates - Details dynamic use cases.
Trailhead: Build Prompt Templates in Agentforce - Covers Flex for custom scenarios.
Salesforce Help: Prompt Template Types - Confirms Flex versatility.
NEW QUESTION # 26
An Agentforce implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce. TheAgentforce Specialistneeds to configure the system to use the most accurate and up-to-date information for email generation.
Which grounding technique should theAgentforce Specialistuse?
- A. Automatic grounding using Draft with Einstein feature
- B. Ground with Record Merge Fields
- C. Ground with Apex Merge Fields
Answer: A
Explanation:
ForEinstein Sales Emailsto generate personalized follow-up emails, it is crucial to ground the email content with the most up-to-date and accurate information. Grounding refers to connecting the AI model with real- time data. The most appropriate technique in this case isGround with Record Merge Fields. This method ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.
* Record Merge Fieldsensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.
* Apex Merge Fieldsare typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.
* Automatic grounding using Draft with Einsteinis a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data likeRecord Merge Fields.
References:
* Salesforce Einstein Sales Emails Documentation:https://help.salesforce.com/s/articleView?id=release- notes.rn_einstein_sales_emails.htm
NEW QUESTION # 27
Which element in the Omni-Channel Flow should be used to connect the flow with the agent?
- A. Assignment
- B. Decision
- C. Route Work Action
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:UC is integrating an Agentforce agent with Omni- Channel Flow to route work. Let's identify the correct element.
* Option A: Route Work ActionThe "Route Work" action in Omni-Channel Flow assigns work items (e.
g., cases, chats) to agents or queues based on routing rules. When connecting to an Agentforce agent, this action links the flow to the agent's queue or presence, enabling interaction. This is the standard element for agent integration, making it the correct answer.
* Option B: AssignmentThere's no "Assignment" element in Flow Builder for Omni-Channel.
Assignment rules exist separately, but within flows, routing is handled by "Route Work," making this incorrect.
* Option C: DecisionThe "Decision" element branches logic, not connects to agents. It's a control structure, not arouting mechanism, making it incorrect.
Why Option A is Correct:"Route Work" is the designated Omni-Channel Flow action for connecting to agents, including Agentforce agents, per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Omni-Channel Integration- Specifies "Route Work" for agents.
* Trailhead: Omni-Channel Flow Basics- Details routing actions.
* Salesforce Help: Set Up Omni-Channel Flows- Confirms "Route Work" usage.
NEW QUESTION # 28
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?
- A. Storing this data requires Data Cloud to be provisioned.
- B. Storing this data requires Salesforce big objects.
- C. Storing this data requires a custom object for data to be configured.
Answer: A
Explanation:
When implementingEinstein Generative AIfor improved customer insights and interactions, theData Cloud is a key consideration for storing and managing large-scale audit and feedback data. TheSalesforce Data Cloud(formerly known asCustomer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioningData Cloud, organizations likeUniversal Containers (UC)can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
* Audit and feedback datagenerated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and theData Cloudprovides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
* Custom objectsorSalesforce Big Objectsare not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions.Big Objectsare more suited for archival data, whereasData Cloudensures more robust processing, segmentation, and analysis capabilities.
:
Salesforce Data Cloud Documentation:https://www.salesforce.com/products/data-cloud/overview/ Salesforce Einstein AI Overview:https://www.salesforce.com/products/einstein/overview/
NEW QUESTION # 29
An Agentforce Specialist needs to create a prompt template to fill a custom field named Latest Opportunities Summary on the Account object with information from the three most recently opened opportunities. How should the Agentforce Specialist gather the necessary data for the prompt template?
- A. Select the latest Opportunities related list as a merge field.
- B. Create a flow to retrieve the opportunity information.
- C. Select the Account Opportunity object as a resource when creating the prompt template.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account. The most robust and flexible way to achieve this is by using aFlow(Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by CreatedDate descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.
* Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they don't natively support querying or aggregating related list data like the three most recent opportunities.
* Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer (perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the Opportunity object as a resource, prompt templates don't directly query related objects without additional logic (e.g., a Flow), making this incorrect.
* Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close Date) for the AI to summarize.
Thus, Option B is the correct method to gather the necessary data efficiently and accurately.
References:
* Salesforce Agentforce Documentation: "Integrate Flows with Prompt Templates" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_flow_prompt_integration.htm&type=5)
* Trailhead: "Build Flows for Agentforce"(https://trailhead.salesforce.com/content/learn/modules/flows- for-agentforce)
NEW QUESTION # 30
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI-driven training content. However, users report that the AI frequently returns outdated documents. Which corrective action should UC implement to improve content relevancy?
- A. Configure a custom retriever that includes a filter condition limiting retrieval to documents updated within a defined recent period, ensuring that only current content is used for AI responses.
- B. Continue using the default retriever without filters, because periodic re-uploads will eventually phase out outdated documents without further configuration or the need for custom retrievers.
- C. Switch the data library source from file uploads to a Knowledge-based data library, because Salesforce Knowledge bases automatically manage document recency, ensuring current documents are returned.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
UC's issue is that their file upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud's vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter (Option B). In Agentforce, a custom retriever allows UC to define specific conditions-such as a filter on a "Last Modified Date" or similar timestamp field-to limit retrieval to documents updated within a recent period (e.g., last 6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.
* Option A: Switching to a Knowledge-based Data Library (using Salesforce Knowledge articles) could work, as Knowledge articles have versioning and expiration features to manage recency.
However, this assumes UC's training content is already in Knowledge articles (not PDFs) and requires migrating all uploaded files, which is a significant shift not justified by the question's context. File- based libraries are still viable with proper filtering.
* Option B: This is the best corrective action. A custom retriever with a date filter leverages the existing file-based library, refining retrieval without changing the data source, making it practical and targeted.
* Option C: Relying on periodic re-uploads with the default retriever is passive and inefficient. It doesn't guarantee recency (old files remain indexed until manually removed) and requires ongoing manual effort, failing to proactively solve the issue.
Option B provides a precise, scalable solution to ensure content relevancy in UC's AI-driven training system.
:
Salesforce Agentforce Documentation: "Custom Retrievers for Data Libraries" (Salesforce Help: https://help.
salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5) Salesforce Data Cloud Documentation: "Filter Retrieval for AI" (https://help.salesforce.com/s/articleView?
id=sf.data_cloud_retrieval_filters.htm&type=5)
Trailhead: "Manage Data Libraries in Agentforce" (https://trailhead.salesforce.com/content/learn/modules
/agentforce-data-libraries)
NEW QUESTION # 31
Northern Trail Outfitters (NTO) wants to configure Einstein Trust Layer in its production org but is unable to see the option on the Setup page.
After provisioning Data Cloud, which step must an Al Specialist take to make this option available to NTO?
- A. Turn on Prompt Builder.
- B. Turn on Einstein Generative AI.
- C. Turn on Agent.
Answer: B
Explanation:
For Northern Trail Outfitters (NTO) to configure the Einstein Trust Layer, the Einstein Generative AI feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.
* Option A (Turning on Agent) is unrelated to the setup of the Einstein Trust Layer, which focuses more on generative AI interactions and data handling.
* Option C (Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it does not enable the Einstein Trust Layer.
Salesforce Agentforce Specialist References:For more details on the Einstein Trust Layer and setup steps:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_overview.htm
NEW QUESTION # 32
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response. Which information does the Resolution text provide?
- A. It shows the full text that is sent to the Trust Layer.
- B. It shows which sensitive data is masked before it is sent to the LLM.
- C. It shows the response from the LLM based on the sample record.
Answer: C
Explanation:
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs: Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
Option B: The "Response" output in the preview shows the LLM's generated text based on the sample record, not the Resolution. Resolution encompasses more than just the LLM response-it includes the entire payload sent to the Trust Layer.
Option C: While the Trust Layer does mask sensitive data (e.g., PII) as part of its guardrails, the Resolution text doesn't specifically isolate "which sensitive data is masked." Instead, it shows the full text, including any masked portions, as processed by the Trust Layer-not a separate masking log.
Option A: This is correct, as Resolution provides a holistic view of the text sent to the Trust Layer, aligning with its role in monitoring and auditing the AI interaction.
Thus, Option A accurately describes the purpose of the Resolution text in the prompt template preview.
Salesforce Agentforce Documentation: "Preview Prompt Templates" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_prompt_preview.htm&type=5)
Salesforce Einstein Trust Layer Documentation: "Trust Layer Outputs" (https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer.htm&type=5)
NEW QUESTION # 33
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
- A. Identify the best matching actions and correct order of execution
- B. Determine a user's access and sort actions by priority to be executed
- C. Find similar requests and provide actions that need to be executed
Answer: A
Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user' s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models
NEW QUESTION # 34
Universal Containers (UC) would like to implement the Sales Development Representative (SDR) Agent.
Which channel consideration should UC be aware of while implementing it?
- A. SDR Agent must also be deployed on the company website.
- B. SDR Agent only works in the Email channel.
- C. SDR Agent must be deployed in the Messaging channel.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) is implementing the Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify leads and schedule meetings. Channel considerations are critical for deployment. Let's evaluate the options based on official Salesforce documentation.
* Option A: SDR Agent must be deployed in the Messaging channel.The Agentforce SDR Agent is designed to engage prospects in real-time conversations, primarily through the Messaging channel (e.g., Salesforce Messaging for in-app or web chat). This aligns with its purpose of qualifying leads interactively and scheduling meetings, as outlined in Agentforce for Sales documentation. While it may leverage email for follow-ups, its core deployment and interaction occur via Messaging, making this a key consideration UC must be aware of. This is the correct answer.
* Option B: SDR Agent only works in the Email channel.The SDR Agent is not limited to email.
While it can send emails (e.g., follow-ups after lead qualification), its primary function-real-time lead engagement-relies on Messaging. Stating it "only works in the Email channel" is inaccurate and contradicts its documented capabilities, making this incorrect.
* Option C: SDR Agent must also be deployed on the company website.While the SDR Agent can be embedded on a company website via Messaging (e.g., as a chat widget), this is an implementation choice, not a mandatory requirement. The agent's deployment is channel-specific (Messaging), and website integration is optional, not a "must." This option overstates the requirement, making it incorrect.
Why Option A is Correct:The SDR Agent's primary deployment in the Messaging channel is a documented consideration for its real-time lead qualification capabilities. UC must plan for this channel to ensure effective implementation, as per Salesforce guidelines.
References:
* Salesforce Agentforce Documentation: SDR Agent Setup > Channels- Specifies Messaging as the primary channel.
* Trailhead: Explore Agentforce Sales Agents- Notes SDR Agent's Messaging focus for lead engagement.
* Salesforce Help: Agentforce for Sales > SDR Agent- Confirms Messaging deployment requirement.
NEW QUESTION # 35
Universal Containers (UC) has a legacy system that needs to integrate with Salesforce. UC wishes to create a digest of account action plans using the generative API feature.
Which API service should UC use to meet this requirement?
- A. Metadata API
- B. SOAP API
- C. REST API
Answer: C
Explanation:
To create a digest of account action plans using the generative API feature,Universal Containersshould use theREST API. TheREST APIis ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.
* Metadata APIis used for retrieving and deploying metadata, not for data operations like generating summaries.
* SOAP APIis an older API used for integration but is less flexible compared to REST for this specific use case.
For more details, refer toSalesforce REST API documentationregarding using REST for data integration and generating content.
NEW QUESTION # 36
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 text encoding (for example, UTF-8, ASCII) option
- B. The maximum number of related list merge fields
- C. The choice between XML and JSON rendering formats for the list
Answer: B
Explanation:
When configuring a custom prompt template to use related lists, the Agentforce Specialist must be aware of the maximum number of related list merge fields that 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 B is correct because there is a limit on how many related list merge fields can be included in a prompt template.
Option A (text encoding) and Option 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
NEW QUESTION # 37
Universal Containers' Agent Action includes several Apex classes for the new Agentforce Agent. What is an important consideration when deploying Apex that is invoked by an Agent Action?
- A. Apex classes invoked by an Agent Action may be deployed with less than 75% test coverage as long as the agent is not activated in production.
- B. The Apex classes may bypass the 75% code coverage requirement as long as they are only used by the agent.
- C. The Apex classes must have at least 75% code coverage from unit tests, and all dependencies must be in the deployment package.
Answer: C
Explanation:
Universal Containers (UC) is using Apex classes within an Agent Action for their Agentforce Agent.
Deploying Apex in Salesforce has specific requirements, especially when tied to Agentforce functionality. Let' s evaluate the options.
* Option A: The Apex classes must have at least 75% code coverage from unit tests, and all dependencies must be in the deployment package.Salesforce enforces a strict requirement that all Apex classes must achieve at least 75% code coverage from unit tests for deployment to production, regardless of their use case (e.g., Agentforce, triggers, or web services). Additionally, when Apex is invoked by an Agent Action (e.g., via a Flow or direct invocation), all dependencies (e.g., referenced classes, objects) must be included in the deployment package to ensure functionality. This is a standard deployment consideration in Salesforce and applies to Agentforce, making this the correct answer.
* Option B: Apex classes invoked by an Agent Action may be deployed with less than 75% test coverage as long as the agent is not activated in production.Salesforce's 75% code coverage requirement is mandatory for production deployment, regardless of whether the agent is activated.
There's no exemption based on activation status-coverage is enforced at the deployment stage. This option is incorrect and contradicts Salesforce's Apex deployment rules.
* Option C: The Apex classes may bypass the 75% code coverage requirement as long as they are only used by the agent.No such bypass exists in Salesforce. The 75% code coverage rule applies universally to all Apex in production, including classes used by Agentforce. Agent-specific usage doesn' t waive this requirement, making this incorrect.
Why Option A is Correct:
The 75% code coverage requirement and inclusion of dependencies are fundamental Salesforce deployment rules, applicable to Apex in Agent Actions. This ensures reliability and functionality in production, as per official documentation.
References:
Salesforce Agentforce Documentation: Agent Builder > Custom Actions > Apex - Notes standard Apex deployment rules apply.
Salesforce Developer Guide: Apex Testing - Confirms 75% coverage requirement.
Trailhead: Deploy Apex Code - Emphasizes coverage and dependencies for production.
NEW QUESTION # 38
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. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
- C. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.
Answer: A
Explanation:
To begin validating that the correct fields are being masked inEinstein Trust Layer, the Agentforce Specialist should request theEinstein Generative AI Audit Datafrom theSecurity sectionof 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 Bis correct because it allows for the retrieval of audit data that can be used to validate data masking.
* Option A(Flow Debugger) andOption C(Einstein Feedback) do not relate to validating field masking in the context of theEinstein Trust Layer.
:
Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm
NEW QUESTION # 39
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