Salesforce Agentforce-Specialist Dumps

According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well-defined output instructions and examples. The documentation specifies: “To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure.”

AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: “Always define your output schema clearly — for example, specify JSON or key-value pairs — and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language.”

Option A (“Ask the LLM to extract and only output important information”) is too vague and can still produce variable or verbose responses. Option C (“Ensure the LLM has been told to only use name value pairs”) is partially correct but incomplete without clear formatting and example output. Therefore, Option B is the correct choice as it aligns with AgentForce’s documented standards for prompt accuracy and reliability.

References (AgentForce Documents / Study Guide):

AgentForce Prompt Engineering Best Practices Guide

AgentForce Developer Study Guide: “Defining Structured Outputs in Prompt Templates”

AgentForce Technical Documentation: “Using Output Instructions and Examples for LLM Control”

In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions. Let’s evaluate its role.

Option A: Find similar requested topics and provide the actions that need to be executed.While the LLM can identify similar topics, its role extends beyond merely finding them—it matches intents to specific topics and determines execution. This option understates the LLM’s responsibility for ordering actions, making it incomplete and incorrect.

Option B: Identify the best matching topic and actions and correct order of execution.The LLM analyzes user input to understand intent, matches it to the best-fitting topic (configured in Agent Builder), and selects associated actions. It also determines the correct sequence of execution based on the agent’s plan (e.g., retrieve data before updating a record). This end-to-end process—from intent recognition to action orchestration—is the LLM’s core role in Agentforce, making this the correct answer.

Option C: Determine a user’s topic access and sort actions by priority to be executed.Topic access is governed by Salesforce permissions (e.g., user profiles), not the LLM. While the LLM prioritizes actions within its plan, its primary role is intent matching and execution ordering, not access control, making this incorrect.

Why Option B is Correct:

The LLM’s role in identifying topics, selecting actions, and ordering execution is central to Agentforce’s autonomous functionality, as detailed in Salesforce documentation.

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.