Free Practice Questions for Salesforce Agentforce-Specialist Exam

Agentforce’s LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner’s role in converting intents into actionable steps while adhering to security and business logic.

In Salesforce Agentforce, a Data Library is a feature that allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data for AI-driven agents. Once the Data Library is created and configured, the uploaded files are indexed to make their content searchable and usable by the AI (e.g., for retrieval-augmented generation or prompt enhancement). The key question is where this indexing occurs. Salesforce Agentforce integrates tightly with Data Cloud, a unified data platform that includes a vector database optimized for storing and indexing unstructured data like uploaded files. When a Data Library is set up, the files are ingested and indexed into Data Cloud’s vector database, enabling the AI to efficiently retrieve relevant information from them during conversations or actions.

Option A: Indexing files in a "location specified by the user" is not a feature of Agentforce Data Libraries. The indexing process is managed by Salesforce infrastructure, not a user-defined location.

Option B: This is correct. Data Cloud handles the indexing of uploaded files, storing them in its vector database to support AI capabilities like semantic search and content retrieval.

Option C: Salesforce File Storage (e.g., where ContentVersion records are stored) is used for general file storage, but it does not inherently index files for AI use. Agentforce relies on Data Cloud for indexing, not basic file storage.

Thus, Option B accurately reflects the process after a Data Library is created and configured in Agentforce.

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: "Vector Database for AI" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_vector_database.htm &type=5)

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.

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)

UC’s custom prompt for summarizing case records is underperforming, and we need to identify a likely cause. Let’s evaluate the options based on Agentforce and Einstein Generative AI mechanics.

Option A: The prompt template version is incompatible with the chosen LLM.Prompt templates in Agentforce are designed to work with the Atlas Reasoning Engine, which abstracts the underlying large language model (LLM). Salesforce manages compatibility between prompt templates and LLMs, and there’s no user-facing versioning that directly ties to LLM compatibility. This option is unlikely and not a common issue per documentation.

Option B: The data being used for grounding is incorrect or incomplete.Grounding is the process of providing context (e.g., case record data) to the AI via prompt templates. If the grounding data—sourced from Record Snapshots, Data Cloud, or other integrations—is incorrect (e.g., wrong fields mapped) or incomplete (e.g., missing key case details), the summaries will be inaccurate. For example, if the prompt relies on Case.Subject but the field is empty or not included, the output will miss critical information. This is a frequent cause of poor performance in generative AI and aligns with Salesforce troubleshooting guidance, making it the correct answer.

Option C: The Einstein Trust Layer is incorrectly configured.The Einstein Trust Layer enforces guardrails (e.g., toxicity filtering, data masking) to ensure safe and compliant AI outputs. Misconfiguration might block content or alter tone, but it’s unlikely to cause summaries to lack appropriate information unless specific fields are masked unnecessarily. This is less probable than grounding issues and not a primary explanation here.

Why Option B is Correct:

Incorrect or incomplete grounding data is a well-documented reason for subpar AI outputs in Agentforce. It directly affects the quality of case summaries, and specialists are advised to verify grounding sources (e.g., field mappings, Data Cloud queries) when troubleshooting, as per official guidelines.

The Model Playground allows an Agentforce Specialist to test and simulate real-world user utterances against the configured actions, ensuring the custom Agent action is triggered appropriately based on intent matching and natural language understanding—without needing a full deployment.

Record Snapshots in Salesforce Prompt Builder leverage the data visible on the user's page layout for an object to ground the prompt. This means that the fields and related lists that are configured on the page layout directly influence the data included in the snapshot.

According to the AgentForce Testing Center Reference Guide, each test case in the Testing Center should define a clear expected output to validate that the agent selects and executes the correct topic in response to a given user utterance.

The Expected Topic API Name acts as the validation reference — it ensures that the reasoning engine correctly classifies the user’s intent and routes the conversation to the appropriate topic. This allows the test to confirm end-to-end functionality, from intent detection to action execution.

Option B, Expected Flow API Name, applies only when testing automation flows directly, not general agent reasoning. Option C, Expected Prompt Template Name, is relevant for template validation but does not confirm correct topic classification, which is the first step in response accuracy.

Therefore, per AgentForce best practices, the correct expected output field to define for Testing Center validation is Option A – Expected Topic API Name.

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.

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)

According to the AgentForce for Service Configuration Guide, when implementing Service Agents on Email, administrators must create an Email Configuration to connect the agent with the appropriate email channel and templates. The documentation specifies: “To enable Service Agents to handle emails, create an Email Configuration that links the agent to the email address, template, and routing parameters. This configuration allows the Service Agent to read, interpret, and respond using the defined template.”

Option B (creating an Omni-Channel flow) applies to routing live messages or chats, not configuring email agents.

Option C is incorrect because Service Agents do not automatically connect to email templates — a manual configuration is required.

Thus, Option A is correct as it aligns with Salesforce’s documented process for connecting email templates to AgentForce Service Agents.

References (AgentForce Documents / Study Guide):

AgentForce for Service Setup Guide: “Creating Email Configurations for Service Agents”

Salesforce Service Cloud Email Configuration Overview

AgentForce Study Guide: “Deploying Service Agents on Email Channels”

As outlined in the AgentForce External Services and Prompt Flow Integration Guide, when data is retrieved from a registered external service via REST API, the response payload is stored as External Service Records. These records can then be referenced dynamically within prompt templates through External Service Record merge fields.

This approach allows the large language model (LLM) to use the fetched data as contextual grounding during prompt execution, ensuring that generated responses are accurate and consistent with the latest API results.

Option B is incorrect because AgentForce does not use XML merge fields for API responses; JSON data is automatically mapped to object structures. Option C is also incorrect — the “Add Prompt Instructions” element modifies prompt context or tone but does not pass external data for merge use.

Therefore, the correct method is Option A – Use External Service Record merge fields, ensuring the external service data is directly available for prompt templates.