Free Salesforce Agentforce-Specialist Practice Exam with Questions & Answers | Set: 7

After modifying masked fields in Einstein's Trust Layer, the next important step is to test and confirm that the responses generated by prompts utilizing the newly masked data still meet quality standards. This ensures that masking sensitive information does not negatively impact the usefulness or accuracy of the AI-generated content. Thorough testing helps identify any issues in prompt performance that could arise due to masking, and adjustments can be made if needed.

Option B is correct because testing the effects of masking on AI responses is a critical step in ensuring AI continues to function as expected.

Option A (turning off and on the Einstein Trust Layer) is unnecessary after changing the masked fields.

Option C (turning on Einstein Feedback) allows for user feedback but is not a direct step following field masking modifications.

According to the AgentForce Testing Center Guide, structured batch testing is the feature that provides consistent, repeatable pass/fail validation for agent testing. The documentation explains: “Structured batch testing allows specialists to define expected outputs per test utterance and automatically validate the agent’s responses, resulting in deterministic pass/fail outcomes.”

This approach ensures testing consistency across multiple runs and environments, unlike user ratings (Option A) which are subjective, or event log scripts (Option B) which are manual and not standardized.

Thus, Option C is correct because structured batch testing provides Salesforce’s official framework for validating agent accuracy with consistent logic.

References (AgentForce Documents / Study Guide):

AgentForce Testing Center Guide: “Running Structured Batch Tests”

AgentForce QA and Validation Best Practices

AgentForce Study Guide: “Automated Pass/Fail Validation for AI Agents”

UC’s agent answers questions using Knowledge articles, configured in Agent Builder. Let’s identify the topic and action.

Option A: General Q&A topic and Knowledge Article Answers action."General Q&A" is not a standard topic name in Agentforce, and "Knowledge Article Answers" isn’t a predefined action. This lacks specificity and doesn’t match documentation, making it incorrect.

Option B: General CRM topic and Answers Questions with LLM Action."General CRM" isn’t a default topic, and "Answers Questions with LLM" suggests raw LLM responses, not Knowledge-grounded ones. This doesn’t align with the Knowledge focus, making it incorrect.

Option C: General FAQ topic and Answers Questions with Knowledge Action.In Agent Builder, the "General FAQ" topic is a common default or starting point for question-answering agents. The "Answers Questions with Knowledge" action (sometimes styled as "Answer with Knowledge") is a prebuilt action that retrieves and grounds responses with Knowledge articles. This matches UC’s implementation and is explicitly supported in documentation, making it the correct answer.

Why Option C is Correct:

"General FAQ" and "Answers Questions with Knowledge" are the standard topic-action pair for Knowledge-based question answering in Agentforce, per Salesforce resources.

UC wants to streamline the use of Generative AI by reducing the time reps spend typing prompts and ensuring consistency, leveraging their existing prompt knowledge. Let’s evaluate the options.

Option A: Agent Builder and Action: Query Records.Agent Builder in Agentforce Studio creates autonomous AI agents with actions like "Query Records" to fetch data. While this could retrieve information, it’s designed for agent-driven workflows, not for simplifying manual prompt entry or ensuring consistency across user inputs. This doesn’t directly address UC’s concerns and is incorrect.

Option B: Einstein Prompt Builder and Prompt Templates.Einstein Prompt Builder, part of Agentforce Studio, allows users to create reusable prompt templates that encapsulate specific instructions and grounding for Generative AI (e.g., using public models via the Atlas Reasoning Engine). UC can predefine prompts based on their known language, saving time for reps by eliminating repetitive typing and ensuring consistency across sales and service teams. Templates can be embedded in flows, Lightning pages, or agent interactions, perfectly addressing UC’s needs. This is the correct answer.

Option C: Einstein Recommendation Builder.Einstein Recommendation Builder generates personalized recommendations (e.g., products, next best actions) using predictive AI, not Generative AI for freeform prompts. It doesn’t support custom prompt creation or address time/consistency issues for reps, making it incorrect.

Why Option B is Correct:

Einstein Prompt Builder’s prompt templates directly tackle UC’s challenges by standardizing prompts and reducing manual effort, leveraging their familiarity with Generative AI language. This is a core feature for such use cases, as per Salesforce documentation.

Why is "Field Generation" the correct answer?

In Agentforce, the Field Generation prompt template type is designed to populate dynamic form fields with AI-generated content, such as summaries or descriptions created by a large language model (LLM).

Key Considerations for Using Field Generation in Dynamic Forms:

AI-Powered Summarization in Form Fields

Field Generation templates allow real-time AI-generated summaries based on customer data.

The summary is dynamically populated in the form field for the sales or service representative to review.

Human-in-the-Loop AI Strategy

Since leadership wants a human to be involved, Field Generation ensures the AI-generated content is editable before submission.

This keeps a human-in-the-loop, allowing manual review before finalizing responses.

Works with Salesforce Dynamic Forms

Field Generation templates integrate seamlessly with Salesforce Dynamic Forms, ensuring AI-powered insights are embedded within form layouts.

Why Not the Other Options?

❌ B. Sales Email

Incorrect because Sales Email templates are designed for AI-generated email content, not for populating form fields.

❌ C. Record Summary

Incorrect because Record Summary templates generate high-level summaries of entire records, but do not populate individual form fields dynamically.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that Field Generation templates are used for AI-powered dynamic form population​.

Einstein Studio (Model Builder) allows organizations to connect and utilize external foundational models while fine-tuning them with company-specific data. This capability is particularly suited to businesses like Universal Containers (UC) that require customization of foundational models to better align with their unique data and use cases.

Option A: Adjusting model temperature is a parameter-level setting for controlling randomness in AI-generated responses but does not necessitate connecting to an external foundational model.

Option B: This is the correct answer because Einstein Studio supports fine-tuning external models with proprietary company data, enabling a tailored and more accurate AI solution for UC.

Option C: Changing frequency penalties is another parameter-level adjustment and does not require external foundational models or Einstein Studio.

The Prompt Builder Best Practices Guide emphasizes including a role instruction in prompts to set tone and communication style. The document explains: “Role-playing instructions (e.g., ‘You are an empathetic customer support agent’) are a best practice for ensuring the LLM adopts the desired persona, tone, and style in responses.”

Option A directly reflects this principle by instructing the LLM to role-play as a character, resulting in empathetic, customer-centric responses.

Option B (listing keywords) provides no tone or intent guidance.

Option C (including full interaction history) risks token overload and redundancy, which can degrade prompt performance.

Thus, Option A aligns with Salesforce’s prompt design best practices for tone and empathy.

References (AgentForce Documents / Study Guide):

Salesforce Prompt Builder Design Guide: “Using Role Instructions for Empathy and Tone”

AgentForce Prompt Engineering Handbook: “Persona and Context in Prompts”

AgentForce Study Guide: “Creating Effective Prompts for Customer-Facing Use Cases”

According to the AgentForce Action Design and Configuration Guide, action instructions serve as the directive text that informs the Reasoning Engine about when and how to invoke a specific action. The documentation states: “Action instructions guide the reasoning engine by describing the action’s purpose, inputs, and when it should be selected. Clear, concise instructions improve the LLM’s accuracy in mapping user intent to the correct action.”

Option A is correct because it reflects this documented purpose — connecting user intent with the appropriate system action.

Option B is incorrect since the temperature parameter for the LLM is defined at the system or configuration level, not within action instructions.

Option C is incorrect because action instructions are meant for the reasoning engine, not for end users.

References (AgentForce Documents / Study Guide):

AgentForce Action Design Guide: “How Action Instructions Support Reasoning”

AgentForce Builder Handbook: “Optimizing Action Selection through Clear Instructions”

AgentForce Study Guide: “Purpose of Action Instructions in Custom Actions”

The AgentForce Observability and Diagnostics Guide details that AgentForce Session Tracing provides the most comprehensive visibility into agent operations. The documentation explains: “Session Tracing captures the entire execution flow for each agent session — including reasoning engine decisions, executed actions, prompts, gateway inputs and outputs, error logs, and final agent responses — to provide an end-to-end view of agent behavior.”

Agentforce Analytics (Option A) focuses on aggregated performance metrics like usage, engagement, and accuracy trends rather than deep operational data.

Utterance Analysis (Option B) evaluates specific interactions or conversation snippets but does not include reasoning engine or system-level traces.

Hence, Option C — AgentForce Session Tracing — is correct as it provides detailed, end-to-end diagnostic insight across all agent executions.

References (AgentForce Documents / Study Guide):

AgentForce Observability Guide: “Using Session Tracing for End-to-End Agent Visibility”

AgentForce Implementation Handbook: “Tracing Reasoning and Action Flows”

AgentForce Study Guide: “Monitoring and Debugging with Session Tracing”

When creating a custom action in Agent, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the Agentforce Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot's functionality without needing custom code.

While Apex triggers and SOQL are important Salesforce tools, Flows are the recommended method for creating custom actions within Agent because they are declarative and highly adaptable.

For further guidance, refer to Salesforce Flow documentation and Agent customization resources.