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Salesforce Certified Agentforce Specialist Exam (page: 2)
Salesforce Certified Agentforce Specialist
Updated on: 25-Dec-2025

Viewing Page 2 of 39
Certified Agentforce Specialist PDF 300 Questions

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)



Universal Containers wants to reduce overall customer support handling time by minimizing the time spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields.
Which combination of Agentforce for Service features enables this effort?

  1. Einstein Reply Recommendations and Case Classification
  2. Einstein Reply Recommendations and Case Summaries
  3. Einstein Service Replies and Work Summaries

Answer(s): B

Explanation:

Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in- chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification (Option A) are the ideal combination to achieve this.

Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC's first goal.

Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC's second goal.

Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don't directly reduce post-chat field entry time.

Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.

Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post-chat automation (Case Classification).

Thus, Option A is the correct answer for UC's needs.


Reference:

Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm&type=5)

Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.case_classification.htm&type=5)

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



Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses. UC notices that the retriever is returning too many irrelevant results, making the responses less useful.
What should UC do to ensure only relevant data is retrieved?

  1. Define filters to narrow the search results based on specific conditions.
  2. Change the search index to a different data model object (DMO).
  3. Increase the maximum number of results returned to capture a broader dataset.

Answer(s): A

Explanation:

In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses. UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the `Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.

Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.

Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC's goal of improving relevance.

Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.

Option A is the most effective step to ensure relevance in retrieved data.


Reference:

Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)

Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)



When creating a custom retriever in Einstein Studio, which step is considered essential?

  1. Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.
  2. Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.
  3. Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.

Answer(s): A

Explanation:

In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:

Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.

Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.

Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.

Filters are noted as optional in Option A, which is accurate--they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.

Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever's output, but it's a secondary step. The retriever must first know where to search (A)

before output can be configured.

Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.

Option A: This is the minimum required step to create a functional retriever, making it essential.

Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.


Reference:

Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)

Trailhead: "Einstein Studio for Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/einstein-studio-for-agentforce)



When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response.
Which information does the Resolution text provide?

  1. It shows the full text that is sent to the Trust Layer.
  2. It shows the response from the LLM based on the sample record.
  3. It shows which sensitive data is masked before it is sent to the LLM.

Answer(s): B

Explanation:

In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs:
Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.

Option B: The "Response" output in the preview shows the LLM's generated text based on the sample record, not the Resolution. Resolution encompasses more than just the LLM response--it includes the entire payload sent to the Trust Layer.

Option C: While the Trust Layer does mask sensitive data (e.g., PII) as part of its guardrails, the Resolution text doesn't specifically isolate "which sensitive data is masked." Instead, it shows the full text, including any masked portions, as processed by the Trust Layer--not a separate masking log.

Option A: This is correct, as Resolution provides a holistic view of the text sent to the Trust Layer, aligning with its role in monitoring and auditing the AI interaction.

Thus, Option A accurately describes the purpose of the Resolution text in the prompt template preview.


Reference:

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

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



Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI- driven training content. However, users report that the AI frequently returns outdated documents.
Which corrective action should UC implement to improve content relevancy?

  1. Switch the data library source from file uploads to a Knowledge-based data library, because Salesforce Knowledge bases automatically manage document recency, ensuring current documents are returned.
  2. Configure a custom retriever that includes a filter condition limiting retrieval to documents updated within a defined recent period, ensuring that only current content is used for AI responses.
  3. Continue using the default retriever without filters, because periodic re-uploads will eventually phase out outdated documents without further configuration or the need for custom retrievers.

Answer(s): B

Explanation:

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.


Reference:

Salesforce Agentforce Documentation: "Custom Retrievers for Data Libraries" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)

Salesforce Data Cloud Documentation: "Filter Retrieval for AI"

(https://help.salesforce.com/s/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)

Trailhead: "Manage Data Libraries in Agentforce"
(https://trailhead.salesforce.com/content/learn/modules/agentforce-data-libraries)



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?

  1. Leverage the Agent Large Language Model (LLM) UI and test UC's agents with different utterances prior to activating the agent.
  2. Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports to review effectiveness.
  3. Create a CSV file with UC's test cases in Agentforce Testing Center using the testing template.

Answer(s): C

Explanation:

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.


Reference:

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.



Viewing Page 2 of 39



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