Salesforce Certified Agentforce Specialist (AI-201) Exam Practice Test

Answer : B

Comprehensive and Detailed In-Depth

Agentforce Agents are designed to handle situations where they cannot interpret a request or retrieve requested data gracefully. Let's assess the options based on Agentforce behavior.

Option A: With a preconfigured message, based on the action type.

While Agentforce allows customization of responses, there's no specific mechanism tying preconfigured messages to action types for unhandled requests. Fallback responses are more general, not action-specific, making this incorrect.

Option B: With a general message asking the user to rephrase the request.

When an Agentforce Agent fails to understand a request or find information, it defaults to a general fallback response, typically asking the user to rephrase or clarify their input (e.g., ''I didn't quite get that---could you try asking again?''). This is configurable in Agent Builder but defaults to a user-friendly prompt to encourage retry, aligning with Salesforce's focus on conversational UX. This is the correct answer per documentation.

Option C: With a generated error message.

Agentforce Agents prioritize user experience over technical error messages. While errors might log internally (e.g., in Event Logs), the user-facing response avoids jargon and focuses on retry prompts, making this incorrect.

Why Option B is Correct:

The default behavior of asking users to rephrase aligns with Agentforce's conversational design principles, ensuring a helpful response when comprehension fails, as noted in official resources.

Salesforce Agentforce Documentation: Agent Builder > Fallback Responses -- Describes general retry messages.

Trailhead: Build Agents with Agentforce -- Covers handling ununderstood requests.

Salesforce Help: Agentforce Interaction Design -- Confirms user-friendly fallback behavior.

Answer : C

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.

Conclusion

The 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 Reference & Documents

Salesforce Trailhead: Work Summaries with Einstein GPT

Provides an overview of how to configure Work Summaries, including the need for custom fields, quick actions, and UI components.

Salesforce Documentation: Deploying Metadata Between Orgs

Explains 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 Guide

Outlines which Einstein Generative AI and Work Summaries configurations are deployable as metadata.

Answer : C

New customer support reps at Universal Containers can use Einstein Work Summaries to quickly understand the details of a case without reading through each case comment. Work Summaries leverage generative AI to provide a concise overview of ongoing cases, summarizing all relevant information in an easily digestible format.

Agent can assist with a variety of tasks but is not specifically designed for summarizing case details.

Einstein Sales Summaries are focused on summarizing sales-related activities, which is not applicable for support cases.

For more details, refer to Salesforce documentation on Einstein Work Summaries.

Answer : A

Comprehensive and Detailed In-Depth

UC wants insights into product and competitor mentions during sales calls, leveraging Einstein Conversation Insights. Let's evaluate the options.

Option A: Enable Einstein Conversation Insights, connect a recording provider, assign permission sets, and customize insights with up to 25 products.

Einstein Conversation Insights analyzes call recordings to identify keywords like product and competitor names. Setup requires enabling the feature, connecting an external recording provider (e.g., Zoom, Gong), assigning permission sets (e.g., Einstein Conversation Insights User), and customizing insights by defining up to 25 products or competitors to track. Salesforce documentation confirms the 25-item limit for custom keywords, making this the correct, precise answer aligning with UC's needs.

Option B: Enable Einstein Conversation Insights, assign permission sets, define recording managers, and customize insights with up to 50 competitor names.

There's no 'recording managers' role in Einstein Conversation Insights setup---integration is with a provider, not a manager designation. The limit is 25 keywords (not 50), and the option omits the critical step of connecting a provider, making it incorrect.

Option C: Enable Einstein Conversation Insights, enable sales recording, assign permission sets, and customize insights with up to 50 products.

'Enable sales recording' is vague---Conversation Insights relies on external providers, not a native Salesforce recording feature. The keyword limit is 25, not 50, making this incorrect despite being closer than B.

Why Option A is Correct:

Option A accurately reflects the setup process and limits for Einstein Conversation Insights, meeting UC's requirement per Salesforce documentation.

Salesforce Help: Set Up Einstein Conversation Insights -- Details provider connection and 25-keyword limit.

Trailhead: Einstein Conversation Insights Basics -- Covers permissions and customization.

Salesforce Agentforce Documentation: Sales Features -- Confirms integration steps.

Answer : A

To improve the productivity of the service center, the Agentforce Specialist should recommend the Service Replies and Case Summaries features.

Service Replies helps agents by automatically generating suggested responses to customer inquiries, reducing response time and improving efficiency.

Case Summaries provide a quick overview of case details, allowing agents to get up to speed faster on customer issues.

Work Summaries are not as relevant for direct customer service operations, and Sales Summaries are focused on sales processes, not service center productivity.

For more information, see Salesforce's Einstein Service Cloud documentation on the use of generative AI to assist customer service teams.

Answer : B

Comprehensive and Detailed In-Depth

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)

Answer : A

Comprehensive and Detailed In-Depth

UC uses Agentforce for Sales to identify similar opportunities, aiding deal closure. Let's determine a criterion used by the 'Find Similar Opportunities' feature.

Option A: Matched opportunities have a status of Closed Won from the last 12 months.

Agentforce for Sales analyzes historical data to find similar opportunities, prioritizing 'Closed Won' deals as successful examples. Documentation specifies a 12-month lookback period for relevance, ensuring recent, applicable matches. This is a key criterion, making it the correct answer.

Option B: Matched opportunities are limited to the same account.

While account context may factor in, Agentforce doesn't restrict matches to the same account---it considers broader patterns across opportunities (e.g., industry, deal size). This is too narrow and incorrect.

Option C: Matched opportunities were created in the last 12 months.

Creation date isn't a primary criterion---status (e.g., Closed Won) and recency of closure matter more. This doesn't align with documented behavior, making it incorrect.

Why Option A is Correct:

'Closed Won' status within 12 months is a documented criterion for Agentforce's similarity matching, providing actionable insights for deal closure.

Salesforce Agentforce Documentation: Agentforce for Sales > Find Similar Opportunities -- Specifies Closed Won, 12-month criterion.

Trailhead: Explore Agentforce Sales Agents -- Details opportunity matching logic.

Salesforce Help: Sales Features in Agentforce -- Confirms historical success focus.