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Salesforce Certified Agentforce Specialist Exam Actual Questions
Salesforce Certified Agentforce Specialist (Page 3 )

Updated On: 17-Jul-2026
Page 3 of 61
PDF with 300 Questions

Universal Containers (UC) is creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements.
Which prompt template type should UC use and which consideration should UC review?

  1. Field Generation, and that Dynamic Fields is enabled
  2. Field Generation, and that Dynamic Forms is enabled
  3. Flex, and that Dynamic Fields is enabled

Answer(s): A

Explanation:

Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template to populate a field with generated output, which directly aligns with the Field Generation prompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC's requirement. Additionally, UC has enabled the Einstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas.

The consideration UC should review is whether Dynamic Fields is enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn't adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.

Option A: Correct. "Field Generation" matches the use case, and "Dynamic Fields" is a key consideration to ensure flexibility and auditability with the Trust Layer.

Option B: "Field Generation" is correct, but "Dynamic Forms" is unrelated. Dynamic Forms is a UI feature for customizing page layouts, not a prompt template setting, making this option incorrect.

Option C: "Flex" templates are more general-purpose and not specifically tailored for field population tasks.
While Dynamic Fields could apply, Field Generation is the better fit for UC's stated goal.

Option A is the best choice, as it pairs the appropriate template type (Field Generation) with a relevant consideration (Dynamic Fields) for UC's scenario with the Einstein Trust Layer.


Reference:

Salesforce Agentforce Documentation: "Prompt Template Types" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)

Salesforce Einstein Trust Layer Documentation: "Monitor AI with Trust Layer" (https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm&type=5)

Trailhead: "Build Prompt Templates for Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/build-prompt-templates-for-agentforce)



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?

  1. Select the latest Opportunities related list as a merge field.
  2. Create a flow to retrieve the opportunity information.
  3. Select the Account Opportunity object as a resource when creating the prompt template.

Answer(s): B

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 a Flow (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.


Reference:

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)



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?

  1. Run a report on the Platform Debug Logs.
  2. Query the Agent log data using the Metadata API.
  3. Run Agent Analytics.

Answer(s): C

Explanation:

Monitoring the usability and action assignments of Agentforce Agents requires insights into how agents perform, how users interact with them, and how actions are executed within conversations. Salesforce provides Agent Analytics (Option C) as a built-in capability specifically designed for this purpose. Agent Analytics offers dashboards and reports that track metrics such as agent response times, user satisfaction, action invocation frequency, and success rates. This tool allows the Agentforce Specialist to assess usability (e.g., are agents meeting user needs?) and monitor action assignments (e.g., which actions are triggered and how often), providing actionable data to optimize the pilot program.

Option A: Platform Debug Logs are low-level logs for troubleshooting Apex, Flows, or system processes. They don't provide high-level insights into agent usability or action assignments, making this unsuitable.

Option B: The Metadata API is used for retrieving or deploying metadata (e.g., object definitions), not runtime log data about agent performance.
While Agent log data might exist, querying it via Metadata API is not a standard or documented approach for this use case.

Option C: Agent Analytics is the dedicated solution, offering a user-friendly way to monitor conversational AI performance without requiring custom development.

Option C is the correct choice for effectively monitoring Agentforce Agents in a pilot program.


Reference:

Salesforce Agentforce Documentation: "Agent Analytics Overview" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_analytics.htm&type=5)

Trailhead: "Agentforce for Admins"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-for-admins)



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?

  1. Set up an Agentforce Data Library for AI retrieval of policy documents.
  2. Expand the AI agent's scope to search all Salesforce records.
  3. Add the files to the content, and then select the data library option.

Answer(s): A

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. The Agentforce 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.


Reference:

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)



A customer service representative is looking at a custom object that stores travel information. They recently received a weather alert and now need to cancel flights for the customers that are related to this Itinerary. The representative needs to review the Knowledge articles about canceling and rebooking the customer flights.
Which Agentforce capability helps the representative accomplish this?

  1. Invoke a flow which makes a call to external data to create a Knowledge article.
  2. Execute tasks based on available actions, answering questions using information from accessible Knowledge articles.
  3. Generate Knowledge article based off the prompts that the agent enters to create steps to cancel flights.

Answer(s): B

Explanation:

The scenario involves a customer service representative needing to cancel flights due to a weather alert and review existing Knowledge articles for guidance on canceling and rebooking. Agentforce provides capabilities to streamline such tasks. The most suitable option is Option B, which allows the agent to "execute tasks based on available actions" (e.g., canceling flights via a predefined action) while "answering questions using information from accessible Knowledge articles." This capability leverages Agentforce's ability to integrate Knowledge articles into the agent's responses, enabling the representative to ask questions (e.g., "How do I cancel a flight?") and receive AI-generated answers grounded in approved Knowledge content. Simultaneously, the agent can trigger actions (e.g., a Flow to update the custom object) to perform the cancellations, meeting all requirements efficiently.

Option A: Invoking a Flow to call external data and create a Knowledge article is unnecessary. The representative needs to review existing articles, not create new ones, and there's no indication external data is required for this task.

Option B: This is correct. It combines task execution (canceling flights) with Knowledge article retrieval, aligning with the representative's need to act and seek guidance from existing content.

Option C: Generating a new Knowledge article based on prompts is not relevant. The representative needs to use existing articles, not author new ones, especially in a time-sensitive weather alert scenario.

Option B best supports the representative's workflow in Agentforce.


Reference:

Salesforce Agentforce Documentation: "Knowledge Replies and Actions" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm&type=5)

Trailhead: "Agentforce for Service"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-for-service)



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