Agentforce-Specialist Practice Test Questions

Explanation:

Comprehensive and Detailed In-Depth Explanation: Universal Containers (UC) aims to leverage Agentforce for Sales to assist sales reps with AI-generated plans that provide guidance and steps for closing deals. Let’s evaluate the options based on Agentforce for Sales features.

Option A: Create Account PlanWhile account planning is valuable for long-term strategy, Agentforce for Sales does not have a specific "Create Account Plan" feature focused on closing individual deals. Account plans typically involve broader account-level insights, not deal-specific closure steps, making this incorrect for UC’s requirement.

Option B: Find Similar Deals "Find Similar Deals" is not a documented feature in Agentforce for Sales. It might imply identifying past deals for reference, but it doesn’t involve generating plans with guidance and steps for closing current deals. This option is incorrect and not aligned with UC’s goal.

Option C: Create Close PlanThe "Create Close Plan" feature in Agentforce for Sales uses AI to generate a detailed plan with actionable steps and guidance tailored to closing a specific deal. Powered by the Atlas Reasoning Engine, it analyzes deal data (e.g., Opportunity records) and provides reps with a roadmap to meet quotas. This directly meets UC’s requirement for AI-generated plans focused on deal closure, making it the correct answer.

Why Option C is Correct:

Create Close Plan is a feature in Agentforce for Sales that provides AI-generated, personalized action plans to help sales reps close deals more effectively. It includes:

1. Step-by-step guidance tailored to the specific deal
2. Recommendations based on sales best practices
3. Insights from historical data or similar opportunities

This feature directly supports sales reps in reaching their quotas by improving how they manage and execute deal strategies.

📘 Salesforce Reference:
“Use the Create Close Plan action to generate a personalized step-by-step plan to help sales reps close deals faster and more effectively.”
— Salesforce Help: Agent Actions for Sales

Implementation Steps:

1. Enable Einstein for Sales in Setup.
2. Add the "Create Close Plan" action to the Opportunity page or Copilot.
3. Train reps to use AI-generated plans in their workflow.

This directly aligns with boosting quota attainment.

Explanation:

Comprehensive and Detailed In-Depth Explanation: Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in-chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification(Option A) are the ideal combination to achieve this.

Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC’s first goal.

Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC’s second goal.

Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don’t directly reduce post-chat field entry time.

Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.

Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post- chat automation (Case Classification).

🔗 Reference
Salesforce Help — Einstein Reply Recommendations
Salesforce Help — Einstein Case Classification Overview

Explanation

Let’s clarify what Record Snapshots grounding is:
When designing prompt templates in Einstein Copilot (Agentforce), you can ground the prompt in the current state of a record (a snapshot).

The snapshot includes:
1. The field names and values of the record.
2. Optionally, related lists configured for grounding.

The goal is to provide the LLM with accurate and relevant context about the record.

However, for efficiency and clarity:
✅ Empty or null data is filtered out.

If a field has no value (null/blank), it’s excluded from the Record Snapshot grounding.

This avoids:
1. Wasting tokens on empty or irrelevant data.
2. Confusing the LLM with fields that provide no context.

Thus, the correct answer is:
B. Empty data, such as fields without values or sections without limits, is filtered out.

Why the other options are incorrect:

Option A (Activities such as tasks and events are excluded) is incorrect:

Activities can be included in Record Snapshots if configured as related lists.
There’s no default rule excluding tasks or events.
Whether they’re included depends on how you configure grounding in the prompt template.

Option C (Email addresses associated with the object are excluded) is incorrect:

Email addresses are not automatically excluded from Record Snapshots.
However, sensitive data like emails can be masked by the Einstein Trust Layer if configured.
But there’s no general rule excluding email fields from the snapshot itself.

Therefore, the main consideration is:
✅ Fields or sections without data are filtered out to streamline the snapshot and avoid sending irrelevant or empty info to the LLM.

🔗 Reference
Salesforce Help — Grounding Prompt Templates with Record Snapshots
Salesforce Blog — Tips for Effective Prompt Grounding

Explanation:

Comprehensive and Detailed In-Depth Explanation: Universal Containers (UC) uses a custom object for Leads and plans to implement the Agentforce Sales Development Representative (SDR) Agent. The SDR Agent is a prebuilt, configurable AI agent designed to assist sales teams by qualifying leads and scheduling meetings. Let’s evaluate the options based on its functionality and limitations.

Option A: Agentforce SDR only works with the standard Lead object. Per Salesforce documentation, the Agentforce SDR Agent is specifically designed to interact with the standard Lead object in Salesforce. It includes preconfigured logic to qualify leads, update lead statuses, and schedule meetings, all of which rely on standard Lead fields (e.g., Lead Status, Email, Phone). Since UC tracks leads in a custom object, this is a critical consideration—they would need to migrate data to the standard Lead object or create awork around (e.g., mapping custom object data to Leads) to leverage the SDR Agent effectively. This limitation is accurate and aligns with the SDR Agent’s out-of-the-box capabilities.

Option B: Agentforce SDR only works on Opportunities. The SDR Agent’s primary focus is lead qualification and initial engagement, not opportunity management. Opportunities are handled by other roles (e.g., Account Executives) and potentially other Agentforce agents (e.g., Sales Agent), not the SDR Agent. This option is incorrect, as it misaligns with the SDR Agent’s purpose.

Option C: Agentforce SDR only supports custom objects associated with Accounts. There’s no evidence in Salesforce documentation that the SDR Agent supports custom objects, even those related to Accounts. The SDR Agent is tightly coupled with the standard Lead object and does not natively extend to custom objects, regardless of their relationships. This option is incorrect.

Why Option A is Correct: The Agentforce SDR Agent’s reliance on the standard Lead object is a documented constraint. UC must consider this when planning implementation, potentially requiring data migration or process adjustments to align their custom object with the SDR Agent’s capabilities. This ensures the agent can perform its intended functions, such as lead qualification and meeting scheduling.

Reference:
Salesforce Help - SDR Agent Setup

Explanation:

Comprehensive and Detailed In-Depth Explanation: In Salesforce Agentforce, acustom retriever is used to fetch relevant data (e.g., from Data Cloud’s vector database or Salesforce records) to ground AI responses. UC’s issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters(Option A) to refine the retriever’s search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the ‘Policy’ category” or “records created after a certain date”) that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC’s problem effectively.

Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.

Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC’s goal of improving relevance.

Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.

✅ The best practice in Salesforce for refining retrievers is to define filters.
Filters allow you to:

1. Narrow the retriever’s search results to a subset of the data.

2. Specify conditions like:
. Record type
. Status fields
. Date ranges
. Specific keywords or tags

3. Ensure the retriever only searches highly relevant records.

For example:

You might add a filter to only include:
Knowledge articles with status = “Published”
Documents tagged with a specific category
Records updated within the past 12 months

This significantly reduces irrelevant results and improves the grounding quality of AI-generated responses. Hence, Option A is correct.

Steps to Implement:

1. In Einstein Studio, edit the custom retriever.
2. Add dynamic filters (e.g., date ranges, record types).
3. Test with sample queries to validate precision.

This ensures the AI grounds responses in high-quality data.

🔗 Reference

Salesforce Help — Configure Search Index Filters for Einstein Copilot
Salesforce Developer Docs — Copilot Retriever Design
Salesforce Blog — How Retrieval-Augmented Generation (RAG) Improves Copilot Responses

Explanation:

When deploying Apex code in Salesforce, including Apex classes invoked by Agentforce Agent Actions, the standard Salesforce deployment requirements still apply:

1. At least 75% code coverage is required across all Apex classes.
2. All dependent components (classes, triggers, objects, etc.) must be included in the deployment package if they are not already present in the target org.
3. This ensures the reliability and integrity of the code being introduced into production, regardless of whether it’s used by an agent or another part of the platform.

❌ Why the other options are incorrect:

B. Apex classes invoked by an Agent Action may be deployed with less than 75% test coverage as long as the agent is not activated in production
❌ Incorrect – Salesforce enforces the 75% coverage rule for all production deployments, regardless of whether the code is currently "active" or tied to an active agent.

C. The Apex classes may bypass the 75% code coverage requirement as long as they are only used by the agent
❌ Incorrect – There is no exemption from the 75% requirement based on usage by agents or other features. All code going into a production org must meet this requirement.

✅ Summary:

When deploying Apex used by Agentforce Agent Actions, ensure:
✅ 75% or greater code coverage
✅ All required dependencies are included

These are standard Salesforce deployment requirements that apply to all Apex code, including that used in Agentforce.

Explanation

Universal Containers wants insights into mentions of products and competitors in sales calls. That’s exactly what Einstein Conversation Insights (ECI) is built for. Let’s break down how to configure it:

✅ Steps to meet UC’s requirement:

1. Enable Einstein Conversation Insights (ECI)
This feature uses AI to analyze call transcripts and audio to detect:
Product mentions
Competitor mentions
Next steps
Pricing discussions
Custom keywords

2. Enable Sales Call Recording
To analyze conversations, Salesforce needs:
. Call recordings from integrated tools (like Zoom, Dialpad, Sales Engagement tools).
. Or transcription files.
Enabling sales recording ensures these calls are captured for AI analysis.

3. Assign Permission Sets
Users need the correct permissions to:
. View Conversation Insights.
. Access call summaries, keyword highlights, and analytics dashboards.

4. Customize Insights for Products and Competitors
1. Einstein Conversation Insights allows you to define:
. Up to 50 custom products.
. Up to 50 custom competitors.

2. When any of these terms are mentioned in a call:
. ECI highlights them in the transcript.
. Displays analytics for frequency and trends.

Hence, Option C is correct because it:

✅ Enables Einstein Conversation Insights.
✅ Enables sales call recording (required for insights).
✅ Assigns permission sets.
✅ Supports up to 50 custom product names for keyword detection.

Why the others are incorrect:

Option A (up to 25 products):

Incorrect limit. Salesforce supports up to 50 products in custom keywords for Conversation Insights.

Option B (up to 50 competitor names only):

Only mentions competitors and leaves out products.
The requirement includes both products and competitors.
Also, defining recording managers is optional, not a core step for enabling insights.

Therefore, the correct configuration for UC is:
C. Enable Einstein Conversation Insights, enable sales recording, assign permission sets, and customize insights with up to 50 products.

🔗 Reference

Salesforce Help — Einstein Conversation Insights Overview
Salesforce Help — Customize Keywords for Conversation Insights
Salesforce Blog — How Einstein Conversation Insights Boosts Sales Productivity

Explanation:

To combine data from multiple unrelated objects (standard and custom) in a prompt template, Universal Containers (UC) should:

Use a Flow with Prompt Template Integration

Why? Flows can:
Query multiple unrelated objects (e.g., Opportunity, Custom_Product_Spec__c).
Transform/format data (e.g., JSON, concatenated text).
Pass the processed data to the prompt template via merge fields.

Implementation Steps:

Create an autolaunched Flow to:
1. Query records from all required objects.
2. Structure the data (e.g., combine fields into a summary).
3. Call the prompt template with the merged data.

Trigger the Flow via Quick Action or Process Builder.

Why Not the Other Options?

A. "Custom object to connect records":
Overly complex. Requires maintaining a temporary object.

C. "Flex template with resources":
Flex templates don’t directly query objects—they rely on pre-processed inputs.

D. "Record Snapshot":
Snapshots only ground data from a single record, not multiple unrelated objects.

Reference:
Salesforce Help - Flows with Prompt Templates

Explanation:

For the Agentforce Service Agent to successfully execute a Flow (like the one created for processing returns), the underlying agent user (the system or integration user tied to the agent) must have the appropriate permissions.

✅ The Run Flows permission allows a user (or agent) to execute autolaunched flows, which is required for Agent Actions that invoke Salesforce Flows.

This permission should be included in the permission set assigned to the Agentforce agent user.

❌ Why the other options are incorrect:

A. Recreate the flow using the Agentforce agent user
❌ Incorrect – The user who created the flow doesn’t impact whether another user or system (like the agent) can run it. Access is controlled by permissions, not authorship.

B. Assign the Manage Users permission to the Agentforce Agent user
❌ Incorrect – Manage Users is a powerful admin-level permission that allows user management. It is not related to flow execution and is unnecessary (and risky) in this context.

✅ Summary:

To allow an Agentforce Service Agent to run a custom flow from an Agent Action, assign the Run Flows permission to the Agentforce agent user.

Explanation:

Comprehensive and Detailed In-Depth Explanation: Salesforce Agentforce leverages generative AI to enhance service agent efficiency, particularly through capabilities that generate recommended replies. In this scenario, Universal Containers aims to reduce handling time by providing replies based on existing Knowledge articles, which are a core component of Salesforce Knowledge. The Knowledge Replies capability is specifically designed for this purpose—it uses generative AI to analyze Knowledge articles, match them to the context of a customer inquiry (e.g., a case or chat), and suggest relevant, pre-formulated responses for service agents to use or adapt. This aligns directly with UC’s goal of leveraging existing content to streamline agent workflows.

Option A (Service Replies): While "Service Replies" might sound plausible, it is not a specific, documented capability in Agentforce. It appears to be a generic distractor and does not tie directly to Knowledge articles.

Option B (Case Replies): "Case Replies" is not a recognized AI capability in Agentforce either. While replies can be generated for cases, the focus here is on Knowledge article integration, which points to Knowledge Replies.

Option C (Knowledge Replies): This is the correct capability, as it explicitly connects generative AI with Knowledge articles to produce recommended replies, reducing agent effort and handling time.
Training service agents on Knowledge Replies ensures they can effectively use AI-suggested responses, review them for accuracy, and integrate them into their workflows, fulfilling UC’s objective.

💡 To reduce agent handling time by generating AI-powered responses based on Knowledge Articles, train agents on how to use and leverage Knowledge Replies.

🔗 Reference>
Salesforce Help — Einstein Knowledge Replies
Salesforce Release Notes — Knowledge Replies Overview