Salesforce Certified Agentforce Specialist (AI-201) Exam Questions Instant Access

Question 1

An administrator wants to check the response of the Flex prompt

template they've built, but the preview button is greyed out.

What is the reason for this?

AThe records related to the prompt have not been selected.

BThe prompt has not been saved and activated,

CA merge field has not been inserted in the prompt.

Answer : A

When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.

Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality.

Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.

Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.

Salesforce Agentforce Specialist Reference:

Refer to the documentation on troubleshooting Flex templates here: https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm

Question 2

Universal Containers needs to ensure that its agent can immediately process customer returns by validating order eligibility before proceeding with the return process. UC wants to maintain a natural conversational flow for customers while ensuring that the order validation step is strictly followed.

What should the Agentforce Specialist do to achieve the desired outcome?

AUse Salesforce Flow to guide the large language model in handling order validation and the return process.

BUse a custom Apex @InvocableMethod to handle the entire return process.

CUse procedural instructions in Agent Script to enforce the order validation step.

Answer : C

The correct answer is C because the requirement combines natural conversation with strict process sequencing. Agent Script procedural instructions are intended for exactly this hybrid pattern: the agent can still converse naturally, but mandatory steps such as order eligibility validation can be enforced before the return process continues. Option A is weak because Flow can execute validation logic, but ''guiding the LLM'' does not guarantee that the LLM will always follow the required sequence. Option B is excessive because moving the entire return process into Apex reduces configurability and is unnecessary when the main need is deterministic orchestration. Salesforce Agent Script guidance states that business-critical logic can execute reliably while conversational elements remain flexible, and run can execute required validations immediately when the code path is reached.

Question 3

An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI

Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client.

How should the Agentforce Specialist integrate the custom LLM into Salesforce?

ACreate an application of the custom LLM and embed it in Sales Cloud via iFrame.

BAdd the fine-tuned LLM in Einstein Studio Model Builder.

CEnable model endpoint on OpenAl and make callouts to the model to generate emails.

Answer : B

Since security and data privacy are critical, the best option for the Agentforce Specialist is to integrate the fine-tuned LLM (Large Language Model) into Salesforce by adding it to Einstein Studio Model Builder. Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce's environment, adhering to data privacy standards.

Option A (embedding via iFrame) is less secure and doesn't integrate deeply with Salesforce's data and security models.

Option C (making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.

Einstein Studio provides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found in Salesforce's Einstein Studio documentation on integrating external models.

Question 4

Universal Containers (UC) has built a custom retriever in AI Models, formerly Einstein Studio, to ground AI responses with technical documentation. UC now requires that a summary field from the search index be included in the output to provide a brief overview of each retrieved document.

What is the recommended course of action for UC?

ACreate a completely new custom retriever with an entirely revised configuration that incorporates the summary field, and delete the current retriever. Editing the existing retriever is not possible.

BEdit the existing retriever version to add the summary field to the list of fields returned by the retriever, save the changes, and activate the new version so that prompt templates use the updated configuration.

CUse only the default retriever, which will automatically include all available fields from the search index, including the summary field, without any need to create or manage custom retrievers.

Answer : B

The correct answer is B. A custom retriever controls which indexed data is searched and what fields are returned to the prompt or agent. If UC needs a summary field returned, the correct action is to update the retriever configuration, save the revised retriever version, and activate that version so the prompt template uses it. Option A is excessive and technically wrong because retriever versioning supports controlled updates rather than forcing a complete rebuild and deletion. Option C is wrong because default retrievers do not automatically return every useful field in the exact structure a business requires. For grounded AI, returned fields must be intentionally configured so the model receives the right concise context without unnecessary noise. Salesforce documents retriever customization and activation of retriever versions.

Question 5

An Agentforce Specialist wants to ensure their custom agent action performs as expected in conversations.

What should the Agentforce Specialist focus on when creating action instructions?

AWrite concise agent action instructions and test in Agentforce Builder.

BEnsure the agent action label matches the utterance's intent.

CInclude comprehensive detailed descriptions and perform smoke testing.

Answer : A

The AgentForce Action Design Guide emphasizes that concise and clear action instructions are essential for predictable and reliable agent behavior. The documentation states: ''Action instructions should be concise, clearly define the purpose of the action, and specify its expected inputs and outputs. Each action must be validated through testing in AgentForce Builder to confirm the LLM interprets and executes it as intended.''

This aligns with Option A, which focuses on clarity and testing.

Option B incorrectly focuses on label-intent matching, which is a naming best practice but not sufficient for accuracy.

Option C promotes overly detailed instructions, which can lead to LLM confusion and inconsistent results.

Therefore, Option A best aligns with Salesforce's official best practices for creating and validating custom actions.

Reference (AgentForce Documents / Study Guide):

AgentForce Action Design and Testing Guide

AgentForce Builder Documentation: ''Validating Custom Actions''

AgentForce Study Guide: ''Creating Clear and Testable Action Instructions''

Question 6

Universal Containers (UC) currently tracks Leads with a custom object. UC is preparing to implement the Sales Development Representative (SDR) Agent. Which consideration should UC keep in mind?

AAgentforce SDR only works with the standard Lead object.

BAgentforce SDR only works on Opportunities.

CAgentforce SDR only supports custom objects associated with Accounts.

Answer : A

Universal Containers (UC) uses a custom object for Leads and plans to implement the Agentforce Sales Development Representative (SDR) Agent. The SDR Agent is a prebuilt, configurable AI agent designed to assist sales teams by qualifying leads and scheduling meetings. Let's evaluate the options based on its functionality and limitations.

Option A: Agentforce SDR only works with the standard Lead object.Per Salesforce documentation, the Agentforce SDR Agent is specifically designed to interact with the standard Lead object in Salesforce. It includes preconfigured logic to qualify leads, update lead statuses, and schedule meetings, all of which rely on standard Lead fields (e.g., Lead Status, Email, Phone). Since UC tracks leads in a custom object, this is a critical consideration---they would need to migrate data to the standard Lead object or create a workaround (e.g., mapping custom object data to Leads) to leverage the SDR Agent effectively. This limitation is accurate and aligns with the SDR Agent's out-of-the-box capabilities.

Option B: Agentforce SDR only works on Opportunities.The SDR Agent's primary focus is lead qualification and initial engagement, not opportunity management. Opportunities are handled by other roles (e.g., Account Executives) and potentially other Agentforce agents (e.g., Sales Agent), not the SDR Agent. This option is incorrect, as it misaligns with the SDR Agent's purpose.

Option C: Agentforce SDR only supports custom objects associated with Accounts.There's no evidence in Salesforce documentation that the SDR Agent supports custom objects, even those related to Accounts. The SDR Agent is tightly coupled with the standard Lead object and does not natively extend to custom objects, regardless of their relationships. This option is incorrect.

Why Option A is Correct:

The Agentforce SDR Agent's reliance on the standard Lead object is a documented constraint. UC must consider this when planning implementation, potentially requiring data migration or process adjustments to align their custom object with the SDR Agent's capabilities. This ensures the agent can perform its intended functions, such as lead qualification and meeting scheduling.

Salesforce Agentforce Documentation: SDR Agent Setup -- Specifies the SDR Agent's dependency on the standard Lead object.

Trailhead: Explore Agentforce Sales Agents -- Describes SDR Agent functionality tied to Leads.

Salesforce Help: Agentforce Prebuilt Agents -- Confirms Lead object requirement for SDR Agent.

Question 7

An Agentforce configured Data Masking within the Einstein Trust Layer.

How should the Agentforce Specialist begin validating that the correct fields are being masked?

AUse a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.

BRequest the Einstein Generative AI Audit Data from the Security section of the Setup menu.

CEnable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.

Answer : C

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