Latest Salesforce Agentforce-Specialist Exam questions and answers [Q54-Q73]

NO.54 Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the
“Save As” function. However, UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters.
What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?

Use Model Playground to create a model configuration with the specified parameters.

Manually add the hyperparameters to the new template.

Revert to using the standard template without modifications.

NO.55 Choose 1 option.
Universal Containers has a requirement to provide a sales summary for its sales reps who are using Employee Agents, but they are not happy with the default answer.
Which best practice should the AgentForce Specialist recommend?

Create a Record Summary custom prompt template.

Create a Knowledge Answer custom prompt template.

Update the standard record summary action.

NO.56 Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding.
What should UC consider when using standard Service AI Grounding?

Service AI Grounding only works with Case and Knowledge objects.

Service AI Grounding only supports String and Text Area type fields.

Service AI Grounding visibility works m system mode.

NO.57 Universal Containers (UC) has implemented Generative AI within Salesforce to enable summarization of a custom object called Guest. Users have reported mismatches in the generated information.
In refining its prompt design strategy, which key practices should UC prioritize?

Enable prompt test mode, allocate different prompt variations to a subset of users for evaluation, and standardize the most effective model based on performance feedback.

Create concise, clear, and consistent prompt templates with effective grounding, contextual role- playing, clear instructions, and iterative feedback.

Submit a prompt review case to Salesforce and conduct thorough testing In the playground to refine outputs until they meet user expectations.

NO.58 The sales team at a hotel resort would like to generate a guest summary about the guests’ interests and provide recommendations based on their activity preferences captured in each guest profile. They want the summary to be available only on the contact record page. Which AI capability should the team use?

Model Builder

Agent Builder

Prompt Builder

NO.59 Universal Container’s internal auditing team asks An Agentforce to verify that address information is properly masked in the prompt being generated.
How should theAgentforce Specialistverify the privacy of the masked data in the Einstein Trust Layer?

Enable data encryption on the address field

Review the platform event logs

Inspect the AI audit trail

NO.60 Universal Containers (UC) wants to ensure the effectiveness, reliability, and trust of its agents prior to deploying them in production. UC would like to efficiently test a large and repeatable number of utterances.
What should the Agentforce Specialist recommend?

Leverage the Agent Large Language Model (LLM) UI and test UC ‘ s agents with different utterances prior to activating the agent.

Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports to review effectiveness.

Create a CSV file with UC ‘ s test cases in Agentforce Testing Center using the testing template.

The goal of Universal Containers (UC) is to test its Agentforce agents for effectiveness, reliability, and trust before production deployment, with a focus on efficiently handling a large and repeatable number of utterances. Let’s evaluate each option against this requirement and Salesforce’s official Agentforce tools and best practices.
Option A: Leverage the Agent Large Language Model (LLM) UI and test UC ‘ s agents with different utterances prior to activating the agent.While Agentforce leverages advanced reasoning capabilities (powered by the Atlas Reasoning Engine), there’s no specific ” Agent Large Language Model (LLM) UI ” referenced in Salesforce documentation for testing agents. Testing utterances directly within an LLM interface might imply manual experimentation, but this approach lacks scalability and repeatability for a large number of utterances.
It’s better suited for ad-hoc testing of individual responses rather than systematic evaluation, making it inefficient for UC’s needs.
Option B: Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports to review effectiveness.Deploying an agent in a QA sandbox is a valid step in the development lifecycle, as sandboxes allow testing in a production-like environment without affecting live data. However, ” Utterance Analysis reports ” is not a standard term in Agentforce documentation. Salesforce provides tools like Agent Analytics or User Utterances dashboards for post-deployment analysis, but these are more about monitoring live performance than pre-deployment testing. This option doesn’t explicitly address how to efficiently test a large and repeatable number of utterances before deployment, making it less precise for UC’s requirement.
Option C: Create a CSV file with UC ‘ s test cases in Agentforce Testing Center using the testing template.
The Agentforce Testing Center is a dedicated tool within Agentforce Studio designed specifically for testing autonomous AI agents. According to Salesforce documentation, Testing Center allows users to upload a CSV file containing test cases (e.g., utterances and expected outcomes) using a provided template. This enables the generation and execution of hundreds of synthetic interactions in parallel, simulating real-world scenarios.
The tool evaluates how the agent interprets utterances, selects topics, and executes actions, providing detailed results for iteration. This aligns perfectly with UC’s need for efficiency (bulk testing via CSV), repeatability (standardized test cases), and reliability (systematic validation), ensuring the agent is production-ready. This is the recommended approach per official guidelines.
Why Option C is Correct:
The Agentforce Testing Center is explicitly built for pre-deployment validation of agents. It supports bulk testing by allowing users to upload a CSV with utterances, which is then processed by the Atlas Reasoning Engine to assess accuracy and reliability. This method ensures UC can systematically test a large dataset, refine agent instructions or topics based on results, and build trust in the agent’s performance-all before production deployment. This aligns with Salesforce’s emphasis on testing non-deterministic AI systems efficiently, as noted in Agentforce setup documentation and Trailhead modules.
References:
Salesforce Trailhead: Get Started with Salesforce Agentforce Specialist Certification Prep – Details the use of Agentforce Testing Center for testing agents with synthetic interactions.
Salesforce Agentforce Documentation: Agentforce Studio > Testing Center – Explains how to upload CSV files with test cases for parallel testing.
Salesforce Help: Agentforce Setup > Testing Autonomous AI Agents – Recommends Testing Center for pre- deployment validation of agent effectiveness and reliability.

NO.61 Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Agentforce Agents. How should the Agentforce Specialist monitor Agents’ usability and the assignment of actions?

Run a report on the Platform Debug Logs.

Query the Agent log data using the Metadata API.

Run Agent Analytics.

NO.62 Universal Containers is evaluating Einstein Generative AI features to improve the productivity of the service center operation.
Which features should theAgentforce Specialistrecommend?

Service Replies and Case Summaries

Service Replies and Work Summaries

Reply Recommendations and Sales Summaries

NO.63 An Agentforce Specialist is creating a custom agent action. The topic is selected correctly, but the action is not.
Which setting should the Agentforce Specialist test and iterate on to ensure the action performs as expected?

Action Scape

Action Instructions

Classification Description

NO.64 Universal Containers (UC) would like to implement the Sales Development Representative (SDR) Agent.
Which channel consideration should UC be aware of while implementing it?

SDR Agent must be deployed in the Messaging channel.

SDR Agent only works in the Email channel.

SDR Agent must also be deployed on the company website.

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.

NO.65 During configuration, Universal Containers (UC) forgot to grant Knowledge access to the Agentforce Service Agent.
Which permission must UC add for the agent to interact with Knowledge articles and answer customer questions effectively?

Allow View Knowledge and Run Flows

Access Knowledge records and fields, and Allow View Knowledge

Access Custom Objects and Manage External Users

NO.66 Universal Containers is deploying a new customer service agent using Agent Script. The Agentforce Specialist needs to update conversational state, navigate between different subagents formerly known as topics, and hand off conversations to a human agent when necessary. To minimize technical debt, the Agentforce Specialist wants to implement these capabilities without building any custom backend code.
Which statement correctly describes how the Agentforce Specialist can implement these specific requirements?

The Agentforce Specialist can use @utils.setVariables, @utils.transition, and @utils.escalate directly.

The Agentforce Specialist can use most utilities directly, but @utils.escalate requires a custom Apex class to define the escalation queue.

The Agentforce Specialist can use most utilities out of the box, but @utils.transition requires a flow to define the target subagent at runtime.

NO.67 Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management.
For security purposes,
UC wants the agent to verify a customer’s identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed.
Which approach best meets this requirement?

Use keywords such as “Always” and “Never” to write clear logic in Topic Instructions to verify user identity before providing any case information,

Create a variable to store the verification status, set it as output from a “Verify Identity” action, and apply a filter so any case-related actions only run when the variable confirms verification.

Store the verification status in a custom variable and set a global instruction that the agent should check this variable before sharing case information.

NO.68 Universal Containers (UC) wants to implement an AI-powered customer service agent that can:
* Retrieve proprietary policy documents that are stored as PDFs.
* Ensure responses are grounded in approved company data, not generic LLM knowledge.What should UC do first?

Set up an Agentforce Data Library for AI retrieval of policy documents.

Expand the AI agent’s scope to search all Salesforce records.

Add the files to the content, and then select the data library option.

Comprehensive and Detailed In-Depth Explanation:
To implement an AI-powered customer service agent that retrieves proprietary policy documents (stored as PDFs) and ensures responses are grounded in approved company data, UC must first establish a foundation for the AI to access and use this data. TheAgentforce Data Library(Option A) is the correct starting point. A Data Library allows UC to upload PDFs containing policy documents, index them into Salesforce Data Cloud’ s vector database, and make them available for AI retrieval. This setup ensures the agent can perform Retrieval-Augmented Generation (RAG), grounding its responses in the specific, approved content from the PDFs rather than relying on generic LLM knowledge, directly meeting UC’s requirements.
* Option B: Expanding the AI agent’s scope to search all Salesforce records is too broad and unnecessary at this stage. The requirement focuses on PDFs with policy documents, not all Salesforce data (e.g., cases, accounts), making this premature and irrelevant as a first step.
* Option C: “Add the files to the content, and then select the data library option” is vague and not a precise process in Agentforce. While uploading files is part of setting up a Data Library, the phrasing suggests adding files to Salesforce Content (e.g., ContentDocument) without indexing, which doesn’t enable AI retrieval. Setting up the Data Library (A) encompasses the full process correctly.
* Option A: This is the foundational step-creating a Data Library ensures the PDFs are uploaded, indexed, and retrievable by the agent, fulfilling both retrieval and grounding needs.
Option A is the correct first step for UC to achieve its goals.
:
Salesforce Agentforce Documentation: “Set Up a Data Library” (Salesforce Help:https://help.salesforce.com/s
/articleView?id=sf.agentforce_data_library.htm&type=5)
Salesforce Data Cloud Documentation: “Ground AI Responses with Data Cloud” (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_agentforce.htm&type=5)

NO.69 Universal Containers’ Service Agent executes a flow action to retrieve Opportunity records. The Opportunity object has an organization-wide default of Private. The agent returns no results despite matching records existing and the flow logic being correctly configured.
Which resolution strictly adheres to the principle of least privilege?

Add the required object permissions to the Einstein Service Agent User’s permission set and configure appropriate sharing rules

Set the agent action flow to run in System Mode without sharing

Change the Opportunity object’s organization-wide default to Public Read Only so the Einstein Service Agent User can access records

NO.70 What is the main purpose of Prompt Builder?

A tool for developers to use in Visual Studio Code that creates prompts for Apex programming, assisting developers in writing code more efficiently.

A tool that enables companies to create reusable prompts for large language models (LLMs), bringing generative AI responses to their flow of work

A tool within Salesforce offering real-time Al-powered suggestions and guidance to users, Improving productivity and decision-making.

NO.71 Universal Containers wants to ensure that its AI agent’s responses consistently reflect the company’s unique brand voice and specific quality standards beyond simple pass or fail checks.
How should the Agentforce Specialist configure the Testing Center to evaluate this specific criteria?

Enable the Coherence quality evaluation

Use the default Response Evaluation metric

Create a custom evaluation using a large language model judge

NO.72 What happens when a chunk of text is vectorized?

It creates numerical representations of chunk content to enable meaning-based retrieval.

It encrypts the content so it can be stored securely within Data 360 data spaces.

It reduces the file size of the original document to reduce Data 360 costs.

NO.73 Choose 1 option.
Universal Containers (UC) plans to answer questions based on similar cases that have been successfully resolved in the past.
What should UC consider when implementing this approach?

No action is needed, as past cases are used to answer the question.

Create a data model object (DMO) based on Case object and create an index on it.

Create an unstructured data model object (UDMO) based on Case object and create an index on it.