Agentforce-Specialist Practice Test Questions

Explanation

UC wants:

Analysis of voice and video call records.

Insights into:
. Competitor mentions
. Coaching opportunities
. Other key call data

The goal is to improve sales performance and competitive awareness.

✅ Call Explorer is the correct feature for this use case because:
It’s part of Einstein Conversation Insights (ECI).
It allows users to:
1. Search and filter call recordings by specific keywords (like competitor names).
2. View metrics on how often certain terms (e.g. competitors, pricing discussions) are mentioned.
3. Identify calls that contain coaching moments, like objection handling or negotiation tactics.
4. Drill into calls for insights and analysis.

Call Explorer specifically surfaces:

Mentions of competitors, products, pricing, or custom keywords.
Trends across multiple calls for competitive intelligence.
Visual graphs showing how often topics occur across conversations.
Easy access to playback and transcript search for coaching purposes.
Hence, C. Call Explorer is the right answer.

Why the other options are incorrect:

A. Call Summaries

This feature provides a concise written summary of an individual call.
It does not provide:
. Competitive analysis across multiple calls.
. Trend analysis for coaching insights.

B. Einstein Sales Insights

This refers to predictive insights like forecasting, scoring, pipeline health.
It’s unrelated to call recording analysis or conversation intelligence.

Thus, for competitor mentions and coaching insights derived from voice and video calls, UC should use: C. Call Explorer

🔗 Reference

Salesforce Help — Einstein Conversation Insights Call Explorer
Salesforce Release Notes — Einstein Conversation Insights Features

Explanation

The support team should use Einstein Reply Recommendations to provide quick, relevant responses to customer inquiries that are grounded in the organization’s knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.

Einstein Service Replies(Option A) is focused on generating replies but doesn't have the same emphasis on grounding responses in the knowledge base.

Einstein Knowledge Recommendations(Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.

Explanation

ThePrompt Builder in Salesforce has some specific limitations, one of which is that custom objects are supported only for Flex template types. This means that users must rely on Flex templates to integrate custom objects into their prompts.

Option A: While rich text area fields have certain restrictions, this does not pertain to the core limitation of integrating custom objects.

Option B: Updates and creations for prompt templates are indeed recorded in the Setup Audit Trail, so this statement is incorrect.

Option C: This is the correct answer as it reflects a documented limitation of the Prompt Builder.

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

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

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

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.

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.

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

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