A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities.
How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?
A. The LLM interprets the user's request, generates a plan by identifying the apcMopnete topics and actions, and executes the actions to retrieve and display the open opportunities
B. The LLM uses a static set of rules to match the user's request with predefined topics and actions, bypassing the need for dynamic interpretation and planning.
C. Using a dialog pattern. the LLM matches the user query to the available topic, action and steps then performs the steps for each action, such as retrieving a fast of open opportunities.
Explanation:
When a sales manager asks the Agent Assistant to "Show me a list of my open opportunities," here’s how the LLM processes the request:
Interpretation & Intent Matching
The LLM analyzes the natural language input to understand the intent (e.g., "list open opportunities").
It maps this to the relevant topic (e.g., "Opportunity Management") and action (e.g., "Query Open Opportunities").
Plan Generation & Execution
The LLM dynamically generates a plan to:
1. Query the Salesforce database for opportunities with StageName NOT IN ('Closed Won', 'Closed Lost').
2. Format the results for display.
This leverages grounding (e.g., {{User.Id}} to filter by the manager’s opportunities).
Why Not the Other Options?
B. "Static rules":
AgentForce uses AI-driven intent matching, not rigid rules.
C. "Dialog pattern":
While dialog patterns exist, the LLM does more than simple matching—it plans multi-step executions.
Key Advantage:
Adaptability: The LLM handles variations like "What’s my pipeline?" or "Show pending deals."
Reference:
Salesforce Help - How Agent Actions Work
An Agentforce at Universal Containers is working on a prompt template to generate personalized emails for product demonstration requests from customers. It is important for the Al-generated email to adhere strictly to the guidelines, using only associated opportunity information, and to encourage the recipient to take the desired action.
How should the Agentforce Specialist include these instructions on a new line in the prompt template?
A. Surround them with triple quotes (""").
B. Make sure merged fields are defined.
C. Use curly brackets {} to encapsulate instructions.
Explanation:
To ensure the AI-generated email adheres to guidelines while staying personalized and actionable, the AgentForce Specialist should:
Use Triple Quotes (""") for Instructions
Triple quotes clearly separate instructions from grounded fields in the prompt template.
Example:
"""
- Use only Opportunity fields (no external data).
- Tone: Professional but enthusiastic.
- Include a clear call-to-action (e.g., 'Schedule your demo today!').
"""
Hi {{Contact.FirstName}},
Thank you for your interest in {{Opportunity.Product_Name__c}}!
Benefit: The LLM treats these as rules, not part of the output.
Why Not the Other Options?
B. "Merged fields":
While necessary for personalization ({{Opportunity.CloseDate}}), they don’t enforce guidelines.
C. "Curly brackets {}":
These are for merge fields, not instructions. The LLM would treat {instructions} as literal text.
Reference:
Salesforce Help - Prompt Template Instructions
An Agentforce wants to use the related lists from an account in a custom prompt template.
What should the Agentforce Specialist consider when configuring the prompt template?
A. The text encoding (for example, UTF-8, ASCII) option
B. The maximum number of related list merge fields
C. The choice between XML and JSON rendering formats for the list
Explanation
Let’s clarify how related lists work in Einstein Copilot (Agentforce) prompt templates:
✅ When grounding a prompt template:
You can include related lists from the parent object (e.g. Account).
For example:
Opportunities related to an Account.
Cases related to an Account.
These related lists are exposed to the LLM through merge fields in the prompt template.
✅ Considerations:
Salesforce limits the number of related list merge fields that can be used in a single prompt template to:
Ensure prompt size remains manageable.
Avoid excessive token consumption.
Prevent model context window overflows.
While the precise limits may vary by tenant or release, Salesforce’s documentation confirms that:
“Each prompt template has a maximum number of related list merge fields you can add.”
Hence, Option B is correct because the Specialist must consider the maximum number of related list merge fields when designing their prompt template.
Why the other options are incorrect:
Option A (Text encoding):
Text encoding (e.g. UTF-8) is not relevant when configuring related list merge fields.
All merge field values in prompts are handled as plain text strings in Salesforce’s native encoding.
Option C (XML vs. JSON rendering):
The related lists are injected into prompts as plain text tables or formatted lists, not structured XML or JSON.
Prompt templates do not support toggling between XML or JSON rendering formats for related lists.
Therefore, the key consideration is:
B. The maximum number of related list merge fields.
🔗 Reference
Salesforce Developer Docs — Prompt Template Best Practices
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
A. Find similar requests and provide actions that need to be executed
B. Identify the best matching actions and correct order of execution
C. Determine a user's access and sort actions by priority to be executed
Explanation:
In Einstein Copilot, the Large Language Model (LLM) plays a central role in interpreting the user's natural language request and determining how to fulfill it using available Copilot Actions.
Why B is Correct:
1. When a user interacts with Einstein Copilot (e.g., types or speaks a request), the LLM interprets the intent behind the request.
2. It then matches the request to the most appropriate Copilot Action(s) available in the org.
3. If multiple actions are needed, the LLM also determines the correct order of execution, enabling it to orchestrate multi-step workflows.
This allows Copilot to behave intelligently and flexibly, understanding complex requests and dynamically assembling the best solution.
A. Find similar requests and provide actions that need to be executed
❌ Incorrect – While LLMs are good at understanding intent, this option is too vague and does not highlight the execution planning or action matching that the LLM actually performs in Einstein Copilot.
C. Determine a user's access and sort actions by priority to be executed
❌ Incorrect – User access and permissions are enforced by Salesforce's platform security layer, not by the LLM. The LLM does not manage access control; it only works within the constraints of what the user is permitted to do.
An administrator wants to check the response of the Flex prompt template they've built, but the preview button is greyed out. What is the reason for this?
A. The records related to the prompt have not been selected.
B. The prompt has not been saved and activated,
C. A merge field has not been inserted in the prompt.
Explanation
When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.
Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality.
Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.
Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.
An Agentforce is setting up a new org and needs to ensure that users can create and execute prompt templates. The Agentforce Specialist is unsure which roles are necessary for these tasks.
Which permission sets should the Agentforce Specialist assign to users who need to create and execute prompt templates?
A. Prompt Template Manager for creating templates and Data Cloud Admin for executing templates
B. Prompt Template Manager for creating templates and Prompt Template User for executing templates
C. Data Cloud Admin for creating templates and Prompt Template User for executing templates
Explanation:
To enable users to create and execute prompt templates, the AgentForce Specialist must assign:
Prompt Template Manager
Grants permissions to:
Create, edit, and manage prompt templates.
Configure grounding and instructions.
Prompt Template User
Allows users to:
Run/execute prompt templates (e.g., generate emails, summaries).
Use templates in Flows, Copilot, or UI buttons.
Why Not the Other Options?
A. "Data Cloud Admin":
Not required for prompt templates—this is for Data Cloud model management.
C. "Data Cloud Admin for creation":
Incorrect. Prompt Template Manager is the correct permission for creation.
Reference:
Salesforce Help - Prompt Template Permissions
A Salesforce Administrator is exploring the capabilities of Einstein Copilot to enhance user interaction within their organization. They are particularly interested in how Einstein Copilot processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Einstein Copilot directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users.
How does Einstein Copilot handle user requests In Salesforce?
A. Einstein Copilot will trigger a flow that utilizes a prompt template to generate the message.
B. Einstein Copilot will perform an HTTP callout to an LLM provider.
C. Einstein Copilot analyzes the user's request and LLM technology is used to generate and display the appropriate response.
Explanation
Let’s clarify how Einstein Copilot works.
✅ Einstein Copilot processes user requests as follows:
A user types a natural-language message or question into Copilot.
Einstein Copilot uses LLM technology to analyze the user’s utterance:
Understands the intent.
Identifies the relevant data or actions needed.
Based on the interpretation:
Copilot might:
Answer directly using generative AI.
Retrieve data from Salesforce records.
Trigger actions (flows, Apex, external calls).
Summarize or transform data using LLM capabilities.
The response itself is generated or formatted by the LLM and displayed back to the user.
Hence, Option C is correct because it describes the core process:
Einstein Copilot analyzes the user's request and uses LLM technology to generate and display the appropriate response.
Why the other options are incorrect:
Option A (Trigger a flow that uses a prompt template):
Partly true, but incomplete.
While Copilot can invoke flows as part of its actions, that’s only one possible pathway.
The essence of Copilot is that it directly engages LLMs to interpret and generate responses.
Not every user question triggers a flow.
Option B (Performs HTTP callout to an LLM provider):
This is technically true behind the scenes, but it’s:
. Abstracted away from the admin/user.
. Not the way to describe how Copilot works functionally.
Also, in many cases, Salesforce uses its own internal LLMs rather than performing external HTTP callouts.
Hence, the correct conceptual answer is:
C. Einstein Copilot analyzes the user's request and LLM technology is used to generate and display the appropriate response.
🔗 Reference
Salesforce Help — How Einstein Copilot Works
Salesforce Blog — Meet Einstein Copilot: Conversational AI for Every User
What is the primary function of the planner service in the Einstein Copilot system?
A. Generating record queries based on conversation history
B. Offering real-time language translation during conversations
C. Identifying copilot actions to respond to user utterances
Explanation:
The Planner Service in Einstein Copilot is responsible for:
Action Identification & Execution
Analyzes user utterances (e.g., "Update the case priority") to:
. Match intent to the best Copilot action (e.g., "Change Case Priority").
. Determine execution order if multiple actions are needed.
Orchestrates the workflow, ensuring actions run in the correct sequence.
Why Not the Other Options?
A. "Generating record queries":
This is handled by retrievers or flows, not the Planner.
B. "Real-time translation":
This is a language model capability, not part of the Planner’s role.
Key Benefit:
Enables multi-step processes (e.g., "Check inventory, then place an order").
Reference:
Salesforce Help - Einstein Copilot Planner
Universal Containers is interested in using Call Explorer to quickly gain insights from meetings recorded by its sales team.
What should theAgentforce Specialistbe aware of before enabling this feature?
A. Call Explorer operates independently of Salesforce Knowledge, requiring no prior setup.
B. Custom Call Explorer actions need to be built before it can be configured.
C. Call Explorer requires the Einstein Conversation Insights permission set to be enabled.
Explanation:
Before enabling Call Explorer for meeting recordings, the AgentForce Specialist must ensure the following:
Einstein Conversation Insights Permission Set
Call Explorer is powered by Einstein Conversation Insights (ECI), which requires:
The "Einstein Conversation Insights" permission set assigned to users.
Meeting recordings (e.g., Zoom, Microsoft Teams) integrated with Salesforce.
Without this permission, users cannot access Call Explorer insights.
Why Not the Other Options?
A. "Call Explorer operates independently of Salesforce Knowledge":
Misleading. While Call Explorer doesn’t directly rely on Knowledge, it does require ECI setup and meeting data integration.
B. "Custom Call Explorer actions need to be built":
Incorrect. Call Explorer provides out-of-the-box insights (e.g., keyword detection, talk patterns). Custom actions are optional.
Steps to Enable Call Explorer:
1. Assign the "Einstein Conversation Insights" permission set to users.
2. Integrate meeting platforms (e.g., Zoom, Teams) with Salesforce.
3. Ensure recordings are synced to Salesforce (via Einstein Call Coaching or third-party tools).
This ensures sales teams can analyze call trends, coach reps, and extract AI-powered insights.
Universal Containers' sales team engages in numerous video sales calls with prospects across the nation. Sales management wants an easy way to understand key information such as deal terms or customer sentiments.
Which Einstein Generative AI feature should An Agentforce recommend for this request?
A. Einstein Call Summaries
B. Einstein Conversation Insights
C. Einstein Video KPI
Explanation:
To help sales management quickly understand key details from video sales calls (e.g., deal terms, sentiments), the AgentForce Specialist should recommend:
Einstein Call Summaries
What it does:
Automatically generates structured summaries post-call, including:
. Deal terms discussed (e.g., pricing, discounts).
. Customer sentiment (positive/neutral/negative).
. Action items (e.g., "Send follow-up proposal").
Integration: Works with Zoom, Microsoft Teams, etc.
Why Not the Other Options?
B. "Einstein Conversation Insights":
Provides analytics (e.g., talk/listen ratios) but not structured summaries.
C. "Einstein Video KPI":
This is a distractor—no such feature exists in Salesforce.
Implementation Steps:
Enable Einstein Call Summaries in Setup.
Connect video platforms (e.g., Zoom).
Train reps to review/edit summaries before saving to records.
Reference:
Salesforce Help - Einstein Call Summaries
An administrator is responsible for ensuring the security and reliability of Universal Containers' (UC) CRM data. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant information from a Salesforce record to be merged with the prompt. Which feature in the Einstein Trust Layer best supports UC's need?
A. Data masking
B. Dynamic grounding with secure data retrieval
C. Zero-data retention policy
Explanation:
The Einstein Trust Layer provides enterprise-grade security for AI in Salesforce. For UC’s requirements, the best fit is:
Dynamic Grounding with Secure Data Retrieval
Safely pulls CRM data (e.g., Case/Account details) into prompts without exposing raw data to the LLM.
Uses real-time, permission-aware grounding to ensure only authorized fields are included (e.g., {{Record.Field}}).
Encrypts data in transit and adheres to Salesforce’s sharing model.
Why Not the Other Options?
A. Data Masking:
Hides sensitive data (e.g., PII) but doesn’t address dynamic record merging for prompts.
C. Zero-Data Retention:
Ensures LLM providers don’t store prompts, but doesn’t solve secure data retrieval from Salesforce records.
How It Works:
The Trust Layer dynamically injects record data into prompts (e.g., {{Account.Name}}).
Data is filtered by field-level security (FLS) and never stored externally.
This balances AI functionality with CRM data protection.
What should An Agentforce consider when using related list merge fields in a prompt template associated with an Account object in Prompt Builder?
A. The Activities related list on the Account object is not supported because it is a polymorphic field.
B. If person accounts have been enabled, merge fields will not be available for the Account object.
C. Prompt generation will yield no response when there is no related list associated with an Account in runtime.
Explanation
When grounding a prompt template with related lists in Prompt Builder (Agentforce), it’s critical to know which related lists are supported and which are not.
✅ Here’s why Option A is correct:
1. The Activities related list on the Account object includes:
Tasks
Events
Emails
2. The relationship between activities and Account is polymorphic:
The WhoId and WhatId fields on activities can refer to multiple types of objects (e.g. Contact, Opportunity, Account, Custom Objects).
3. Because of this polymorphic structure:
The related list isn’t a standard child relationship in Salesforce’s data model.
It’s not directly supported in the grounding for related list merge fields in Prompt Builder.
Hence, Option A is correct — activities related lists cannot be used as related list merge fields in prompt templates due to their polymorphic nature.
Why the other options are incorrect:
Option B (Person accounts block merge fields on Account)
Person accounts still use the Account object.
Prompt Builder supports merge fields on Account even if person accounts are enabled.
No limitation exists that disables merge fields simply because person accounts are active.
Option C (No related list means no prompt response)
If no related list exists, the prompt will still run — it simply won’t inject data for that merge field.
The prompt template might produce an incomplete or less useful response, but it does not yield a blank or null prompt overall.
Thus, the important consideration is:
A. The Activities related list on the Account object is not supported because it is a polymorphic field.
🔗 Reference
Salesforce Developer Docs — Related Lists and Polymorphic Relationships
Salesforce Help — Related Lists for Prompt Templates
| Page 7 out of 25 Pages |
| Previous |