Pass the Salesforce AI Specialist Agentforce-Specialist Questions and answers with CertsForce

In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM’s token capacity (e.g., input + output tokens). The random nature of these failures suggests variability in the token count across different records, which is directly addressed by Option B.

Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record—some records might have short text fields while others have lengthy ones—the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM’s limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.

Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation does not define "Flex" as a specific template type for handling token variability in this context (there are Flow-based templates, but they’re unrelated to token limits). This option is a distractor and not a verified solution.

Option C: The LLM’s token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens for advanced models) and does not vary with user demand. Demand might affect performance or availability, but not the token limit itself.

Option B is the correct answer because it accurately identifies the dynamic nature of the prompt template as the root cause of variable token counts leading to random failures.

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

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

Why is "Sales rep users are missing the Use SDR Agent permission set" the correct answer?

If sales reps are unable to find the Agentforce Sales Development Representative (SDR) Agent, the most likely cause is missing permissions. The "Use SDR Agent" permission set is required for users to access and interact with the SDR Agent in Agentforce.

Key Considerations for This Issue:

Permission Set Restriction

Users must have the "Use SDR Agent" permission set to access Agentforce SDR in their Salesforce environment.

If they lack this permission, the SDR Agent will not appear in their interface.

Agentforce Role-Based Access Control

Agentforce assigns specific permissions based on user roles.

Sales reps require explicit permission to access the SDR Agent.

Fixing the Issue

The Salesforce Admin should assign the "Use SDR Agent" permission set to all relevant sales reps.

This is done in Setup → Permission Sets → Assign to Users.

Why Not the Other Options?

❌ A. Sales rep users' profiles are missing the Allow SDR Agent permission.

Incorrect because "Allow SDR Agent" is not a standard permission setting in Agentforce.

Permission is granted via permission sets, not profile-level settings.

❌ B. Sales rep users do not have access to the SDR Agent object.

Incorrect because there is no separate "SDR Agent object" in Salesforce.

SDR Agents are AI-driven features, not standard CRM objects that require object-level access.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that users require specific permission sets to access Agentforce SDR Agents​.

Salesforce Instructions for Certification highlight the role of permission sets in controlling Agentforce access​.

Context of the Question

Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.

Einstein Generative AI is already enabled in Production, and the “Einstein Work Summaries” permission set is already available in Production.

The Agentforce Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.

What Can Actually Be Deployed from Sandbox to Production?

Custom Fields: Metadata that is easily created in sandbox and then deployed.

Quick Actions: Also metadata-based and can be deployed from sandbox to production.

Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.

Why Option C is Correct

No Need to Turn on Einstein in Sandbox for Deployment: Einstein Generative AI is already enabled in Production; turning it on in the sandbox is typically a manual step if you want to test, but that step itself is not “deployable” in the sense of metadata.

Permission Set Assignments (as in Option A) are not deployable metadata. You can deploy the Permission Set itself but not the specific user assignments. Since the question specifically asks “Which other configuration steps should be taken in the sandbox that can be deployed to the production org?”, user assignment is not one of them.

Why Not Option A or B?

Option A: Mentions creating permission set assignments for agents. This cannot be directly deployed from sandbox to Production, as permission set assignments are user-specific and considered “data,” not metadata.

Option B: Mentions “Turn on Einstein.” But Einstein Generative AI is already enabled in Production. Additionally, “Turning on Einstein” is typically an org-level setting, not a deployable metadata item.

ConclusionThe main deployable items you can reliably create and test in a sandbox, and then migrate to Production, are:

Custom Fields (Issue, Resolution, Summary).

A Quick Action that updates those fields.

Page Layout Change to include the Wrap Up component.

Therefore, Option C is correct and focuses on actions that are truly deployable as metadata from a sandbox to Production.

Salesforce Agentforce Specialist References & Documents

Salesforce Trailhead: Work Summaries with Einstein GPTProvides an overview of how to configure Work Summaries, including the need for custom fields, quick actions, and UI components.

Salesforce Documentation: Deploying Metadata Between OrgsExplains what can and cannot be deployed via change sets (e.g., custom fields, page layouts, quick actions vs. user permission set assignments).

Salesforce Agentforce Specialist Study GuideOutlines which Einstein Generative AI and Work Summaries configurations are deployable as metadata.

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)

Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:

According to the AgentForce Troubleshooting and Optimization Guide, hallucinations — instances where the agent fabricates details such as weblinks or data — often occur due to issues in prompt construction or retrieved content grounding. The recommended diagnostic process involves inspecting the prompt template instructions and reviewing the resolved prompt output, including the actual retrieved knowledge chunks.

By examining these areas, the AgentForce Specialist can determine whether the hallucinated content originates from ambiguous prompt phrasing, missing grounding variables, or irrelevant retrieval results. This approach ensures an evidence-based investigation directly linked to the agent’s reasoning and generation steps.

Option A is incorrect because hallucination guardrails are defined in prompts and actions, not topic names or classification descriptions. Option C is also incorrect since simply adding “ALWAYS” to instructions does not enforce factual grounding and is not a documented troubleshooting method.

Therefore, per AgentForce best practices, the correct process is Option B – Examine the prompt instructions and contents of the chunks in the resolved prompt output.

The AgentForce Retriever Configuration Guide specifies that filters are used to refine and constrain search results within a retriever setup. Filters operate by applying conditions (up to 10) on indexed fields such as document type, category, region, or update date. This targeted filtering ensures that the retrieved data is highly relevant to the current user query or context.

For instance, an AgentForce retriever could be configured to include only documents tagged as “Active” or within a specific product line, reducing noise and improving grounding accuracy for the LLM. This mechanism supports precision retrieval, which directly improves both the accuracy and reliability of generated responses.

Option B is incorrect because filters do not handle encryption or masking of sensitive data — those functions are managed through Data Cloud security and access controls. Option C is incorrect because retrievers do not aggregate or summarize documents; they retrieve data for grounding, leaving summarization to the LLM reasoning layer.

Therefore, the correct answer is Option A – Filters narrow search results using field-based conditions to improve relevancy and retrieval precision.

To generate personalized emails using LLMs while reusing templates:

Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.

Option A: Standard email templates lack LLM integration and dynamic personalization.

Option B: "t field Generation" is not a valid Salesforce prompt template type.

Context of the QuestionUniversal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field.

Field Generation Prompt Template

Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.

Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.

Why Not Flex or Sales Email Prompt Templates?

Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.

Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting outreach or follow-up messages). This template is not specifically built to populate a field on a record.

ConclusionFor automatically populating the Description field with AI-generated content, the Field Generation prompt template (Option A) is the correct choice.

Salesforce Agentforce Specialist References & Documents

Salesforce Documentation: Prompt Template TypesExplains various template types (Field Generation, Flex, Email, etc.) and their typical use cases.

Salesforce Agentforce Specialist Study GuideHighlights Field Generation prompt templates for populating or updating record fields with AI-generated text.

The primary function of the planner service in the Agent system is to identify copilot actions that should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that the Agent should perform based on user input.

The root cause of users receiving incorrect sales summaries lies in non-unique action instructions (Option B). In Einstein Bots, custom actions are triggered based on how well user utterances align with the action instructions defined for each action. If the instructions for the three custom actions overlap or lack specificity, the bot’s natural language processing (NLP) cannot reliably distinguish between them, leading to mismatched responses.

Steps to Investigate:

Review Action Instructions: Ensure each custom action has distinct, context-specific instructions. For example:

Action 1: "Summarize quarterly sales by region."

Action 2: "Generate a product-wise sales breakdown for the current fiscal year."

Action 3: "Provide a comparison of sales performance between online and in-store channels."Ambiguous or overlapping instructions (e.g., "Get sales summary") cause confusion.

Test Utterance Matching: Use Einstein Bot’s training tools to validate if user utterances map to the correct action. Overlap indicates instruction ambiguity.

Refine Instructions: Incorporate keywords or phrases unique to each sales summary type to improve intent detection.

Why Other Options Are Incorrect:

A. Assigning actions to an agent is irrelevant, as custom actions are automated bot components.

C. Input/output types relate to data formatting, not intent routing. While important for execution, they don’t resolve utterance mismatches.

Einstein Bot Developer Guide: Stresses the need for unique action instructions to avoid intent conflicts.

Trailhead Module: "Build AI-Powered Bots with Einstein" highlights instruction specificity for accurate action triggering.

Salesforce Help Documentation: Recommends testing and refining action instructions to ensure clarity in utterance mapping.