Agentforce-Specialist Practice Test Questions

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

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.

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

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.

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

Explanation:

When Einstein generates a Work Summary for cases, it dynamically grounds the summary in multiple data sources to ensure accuracy and relevance:

Conversation Context
Includes chat/email transcripts between the agent and customer.
Captures key details like customer intent, issues discussed, and resolutions proposed.

Knowledge Articles
References relevant Salesforce Knowledge articles linked to the case.
Ensures summaries align with approved solutions.

Case Data
Pulls structured case details (e.g., status, priority, custom fields).
Provides context like case history or related records (e.g., Account/Contact).

Why Not the Other Options?

B. "Existing conversation context only" → Too limited. Omits critical Knowledge and case data.
C. "Conversation context and Knowledge articles" → Misses structured case data, which is essential for summaries.

Explanation:

To help the busy sales rep capture key details from long voice/video calls, the AgentForce Specialist should recommend:

Einstein Call Summary

What it does:
Automatically generates structured summaries after calls, highlighting:
. Key discussion points (e.g., pricing, objections).
. Action items (e.g., "Send contract by Friday").
. Customer sentiment (positive/neutral/negative).

Benefit: Reps can quickly review what they missed without rewatching entire calls.

Why Not the Other Options?

B. "Call Explorer":
Designed for managers to analyze call trends, not for individual rep productivity.

C. "Sales Summary":
Focuses on opportunity data (e.g., stage changes), not call content.

Implementation Steps:

Enable Call Summaries in Setup.
Integrate with Zoom/MS Teams.
Train reps to review/edit summaries post-call.

Reference:
Salesforce Help - Einstein Call Summaries

Explanation

UC wants audit and feedback data for Einstein Generative AI to be accessible for reporting. Let’s clarify how Salesforce handles this:

✅ Einstein Generative AI Audit & Feedback Data is:

Part of the Einstein Trust Layer Includes:
. Prompt text submitted to the LLM.
. Masking details for sensitive data.
. LLM responses.
. Toxicity detection results.
. User feedback on generated content (thumbs up/down).

Designed to help customers audit AI usage for security, compliance, and quality.

✅ Where is this audit data stored?
Audit and feedback data for Einstein Copilot and generative AI is stored in Data Cloud tables.

You must have Data Cloud provisioned to:
. Store this audit trail.
. Query it for reporting or compliance analysis.

This enables you to:
. Create reports and dashboards.
. Analyze trends in AI usage and feedback.

Hence, Option A is correct.

Why the other options are incorrect:

Option B (Custom object):

Audit and feedback data is not stored in custom objects by default.
Salesforce automatically stores it in Data Cloud.

Option C (Big objects):
Big objects are used for storing large-scale transactional or historical data.
Einstein Trust Layer audit data does not use big objects. It’s structured in Data Cloud to support analytics.

Therefore, UC must ensure:
A. Storing this data requires Data Cloud to be provisioned.

🔗 Reference

Salesforce Help — View Generative AI Audit Data
Salesforce Help — Einstein Trust Layer Overview

Explanation:

To safely integrate a custom LLM into Salesforce while addressing security and privacy concerns, the AgentForce Specialist should:

Use Einstein Studio Model Builder

Why? Einstein Studio provides:
Secure, native integration with Salesforce data (no external callouts).
Compliance with the Einstein Trust Layer (data masking, audit trails).
Direct grounding in CRM data (e.g., {{Account.Name}}).

Steps:
Import the fine-tuned LLM into Einstein Studio.
Configure data access permissions.
Deploy as a Prompt Template in Salesforce.

Why Not the Other Options?

A. "iFrame embedding":

Security risk: Exposes Salesforce data to external systems.
Poor UX: iFrames are clunky and lack native integration.

C. "OpenAI callouts":
Violates data privacy: Raw customer data leaves Salesforce.
No Trust Layer protection: Masking/auditing isn’t automatic.

Reference:
Salesforce Help - Einstein Studio

Explanation

Comprehensive and Detailed In-Depth Explanation: In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account.

The most robust and flexible way to achieve this is by using a Flow(Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by Created Date descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.

Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they don’t natively support querying or aggregating related list data like the three most recent opportunities.

Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer (perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the Opportunity object as a resource, prompt templates don’t directly query related objects without additional logic (e.g., a Flow), making this incorrect.

Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close Date) for the AI to summarize.

Thus, Option B is the correct method to gather the necessary data efficiently and accurately.