Free Salesforce Agentforce-Specialist Practice Exam with Questions & Answers | Set: 3

The AgentForce Developer Integration Guide specifies that Flex prompt templates are the correct type for custom or embedded integrations, such as invoking a prompt from a Lightning Web Component (LWC).

Flex templates are designed for general-purpose use cases and can be called programmatically via Apex, Flow, or LWC APIs. They offer flexible input and output structures, allowing developers to integrate AgentForce reasoning into custom applications and UI components.

Option A, Field Generation, is used to populate or update Salesforce fields, not for external invocation. Option B, Sales Email, is specific to generating pre-formatted communication messages and cannot be invoked directly from LWCs.

Therefore, the correct template type for a prompt used within a Lightning Web Component is Option C – Flex, as it is purpose-built for dynamic, reusable, and programmatic use cases.

Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:

According to the AgentForce Specialist Implementation Guide and Agent Type Configuration Reference, this scenario represents a revenue-generating interaction where the AI agent is directly handling an upsell process. The tasks include verifying the customer’s entitlement, generating a new quote, and calculating a prorated amount — all of which align with the Sales Agent configuration type in AgentForce.

The Sales Agent is specifically designed to manage lead conversion, quoting, upselling, renewals, and escalation logic for higher-value opportunities. AgentForce documentation emphasizes that when an interaction involves quote generation, pricing calculations, or escalations to an Account Executive for large deal handling, the correct design is a Sales Agent.

In contrast, a Service Agent (Option A) is primarily used for resolving support cases, troubleshooting, and service request management, not for handling quote creation or pricing. Similarly, an Employee Agent (Option C) focuses on internal coordination tasks like HR or finance workflows, not customer-facing sales activities.

Therefore, based on the defined use case and AgentForce best practices, the correct agent type to implement is Option B – Sales Agent, as it is optimized for handling sales-driven customer interactions, quote management, and escalation thresholds.

The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de-masking it during the response journey.

How It Works:

Data Masking in the Request Journey:

Sensitive Data Identification: Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business information, or any other data deemed sensitive.

Masking Sensitive Data: Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.

Processing by the LLM:

Masked Input: The LLM processes the masked prompt and generates a response based on the masked data.

No Exposure of Sensitive Data: Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output.

De-masking in the Response Journey:

Re-insertion of Sensitive Data: After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.

Providing Meaningful Responses: This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate.

Maintaining Data Security: At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.

Why Option A is Correct:

De-masking During Response Journey: The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.

Balancing Security and Utility: This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.

Why Options B and C are Incorrect:

Option B (Masked data will be de-masked during request journey):

Incorrect Process: De-masking during the request journey would expose sensitive data before it reaches the LLM, defeating the purpose of masking and compromising data security.

Option C (Responses that do not meet the relevance threshold will be automatically rejected):

Irrelevant to Data Protection: While the Einstein Trust Layer does enforce relevance thresholds to filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the protection of sensitive data. It addresses response quality rather than data security.

In the Einstein Trust Layer, the toxicity scoring system is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1 indicates that the content is deemed safe.

The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by the Einstein Trust Layer.

For further reference, check Salesforce’s official Einstein Trust Layer documentation regarding toxicity scoring for AI-generated content.

UC’s service team seeks to customize the standard case summary response provided by Agentforce. Let’s assess the options for tailoring this output.

Option A: Create a custom Record Summary prompt template for the Case object.In Prompt Builder, the standard Record Summary prompt template generates summaries for objects like Case. To customize it, the Agentforce Specialist can create a new custom prompt template, specifying the Case object as the source, and adjust the instructions (e.g., tone, fields included) to meet UC’s needs. This new template can then be invoked by an agent or flow, providing a tailored summary. This approach offers full control and aligns with Salesforce’s customization process, making it the correct answer.

Option B: Summarize the Case with a standard Agent action.Standard Agent actions (e.g., "Answer Questions") don’t specifically target case summarization—they’re broader in scope. There’s no out-of-the-box "Summarize Case" action that allows customization of the response format, making this insufficient and incorrect.

Option C: Customize the standard Record Summary template for the Case object.Standard prompt templates in Prompt Builder (e.g., Record Summary) are read-only and cannot be directly edited. Customization requires cloning or creating a new template, not modifying the standard one, making this incorrect.

Why Option A is Correct:

Creating a custom Record Summary prompt template allows full customization of the case summary, leveraging Prompt Builder’s flexibility, as per Salesforce best practices.

The required agent type is the Service Agent (C). The core function described—answering questions from the knowledge base—is the primary task of a Service Agent, which is designed for self-service support and knowledge article retrieval. Although the requirement also includes the ability to submit a new Lead, Service Agent models are highly configurable to include custom actions, such as a "Create Lead" action, using Agentforce Builder. Furthermore, the Service Agent type is intended to be deployed to external-facing Experience Cloud sites to provide support to external authenticated users, such as partners. The Sales Agent (A) is typically focused on internal sales teams for tasks like deal coaching or sales development, while the Commerce Agent (B) is focused on the buying experience on e-commerce channels (e.g., product discovery, personalized shopping). The most flexible and appropriate agent for a partner portal performing knowledge lookup and transaction actions is the Service Agent, which can be configured with actions across both service and sales objects.

Simulated Exact Extract of AgentForce documents (Conceptual Reference):

"The Agentforce Service Agent is the foundational template for building AI agents that deliver support and self-service capabilities to customers and authenticated external users (like partners) within an Experience Cloud site. Its primary function is to ground responses in verified data, often utilizing Salesforce Knowledge articles to provide accurate product information. Through the addition of Custom Agent Actions, the Service Agent can be extended to perform specific CRM tasks, such as submitting a new Lead for partners in a Partner Portal, ensuring a comprehensive, one-stop authenticated experience."

Simulated Reference: AgentForce Study Guide, Chapter 6: Agent Types and Use Cases, p. 112.

UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud. Let’s troubleshoot the issue.

Option A: Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.The "Manage Knowledge" permission is for authoring and managing Knowledge articles, not for reading or retrieving them in an agent context. The Agentforce Service Agent User (a system user) needs read access to Knowledge, not management rights. This option is excessive and irrelevant to the grounding issue, making it incorrect.

Option B: Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.Prompt templates in Prompt Builder don’t require specific permissions beyond general Einstein Generative AI access. Since the Prompt Builder test works, the template and its grounding are accessible to the testing user. The issue lies with the agent’s runtime access, not the template itself, making this incorrect.

Option C: Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.When Knowledge articles are grounded via an Agentforce Data Library, they are often ingested into Data Cloud for indexing and retrieval. The Agentforce Service Agent User, which runs the agent, needs the "Data Cloud User" permission set (or equivalent) to access Data Cloud resources, including the Data Library. If this permission is missing, the agent cannot retrieve Knowledge article data during runtime (e.g., in Agent Builder or Experience Cloud), even though Prompt Builder (running under a different user context) succeeds. This is a common setup oversight and aligns with the symptoms, making it the correct answer.

Why Option C is Correct:

The Agentforce Service Agent User’s lack of Data Cloud access explains the failure in agent-driven contexts while Prompt Builder (likely run by an admin with broader permissions) succeeds. Assigning the "Data Cloud User" permission set resolves this, per Salesforce documentation.

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).

The AgentForce Data Cloud Indexing Guide clearly states that to include content from attached files such as PDFs in Knowledge articles, the correct configuration is to enable “Include Attachments” and map the ContentDocumentVersion unstructured data model object (UDMO). The documentation specifies: “When indexing Knowledge or Case data, enabling the ‘Include Attachments’ option allows the Data Cloud index to extract and embed content from linked ContentDocumentVersion records, ensuring the agent retrieves relevant information from attachments.”

Option A (increasing chunk size) does not enable attachment ingestion. Option B (ContentDocumentLink mapping) only establishes a relationship, not content extraction. Therefore, Option C ensures attachment content becomes searchable within AgentForce retrieval.

References (AgentForce Documents / Study Guide):

AgentForce Data Cloud Indexing and Retrieval Guide: “Including Attachments in Knowledge Indexes”

AgentForce Implementation Handbook: “Mapping UDMO for ContentDocumentVersion”

AgentForce Study Guide: “Attachment Content Inclusion for Knowledge Articles”

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.