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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Exam Duration: | 105 minutes |
| Related Certifications: | Salesforce AI Associate (retired) Salesforce Platform Administrator Salesforce Platform App Builder Salesforce AI Specialist (retired) |
| Exam Format: | Scenario-based Questions, Multiple Select, Multiple Choice |
| Exam Price: | USD 200 |
| Available Languages: | English, Japanese, French |
| Real Exam Qty: | 60 multiple-choice questions + up to 5 unscored questions |
| Passing Score: | 72%-73% |
| Recommended Training: | Salesforce Trailhead Agentforce Learning Paths Agentforce Specialist Exam Prep |
| Exam Registration: | Certification Exam Guide Salesforce Certification Registration |
| Sample Questions: | Salesforce Agentforce-Specialist Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | None |
| Official Syllabus URL: | https://help.salesforce.com/s/articleView?id=005298924&type=1 |
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NEW QUESTION # 31
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?
Answer: A
Explanation:
When configuring acustom prompt templateto use related lists, the Agentforce Specialist must be aware of themaximum number of related list merge fieldsthat can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it's important to monitor and optimize the number of merge fields used.
* Option Bis correct because there is a limit on how many related list merge fields can be included in a prompt template.
* Option A(text encoding) andOption C(XML/JSON rendering) are not key considerations in this context.
:
Salesforce Prompt Builder Documentation:https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
NEW QUESTION # 32
A data scientist needs to view and manage models in Einstein Studio, and also needs to create prompt templates in Prompt Builder. Which permission sets should an Agentforce Specialist assign to the data scientist?
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:
The data scientist requires permissions for Einstein Studio (model management) and Prompt Builder (template creation). Note: "Einstein Studio" may be a misnomer for Data Cloud's model management or a related tool, but we'll interpret based on context. Let's evaluate.
* Option A: Prompt Template Manager and Prompt Template UserThere's no distinct "Prompt Template Manager" or "Prompt Template User" permission set in Salesforce-Prompt Builder access is typically via "Einstein Generative AI User" or similar. This option lacks coverage for Einstein Studio
/Data Cloud, making it incorrect.
* Option B: Data Cloud Admin and Prompt Template ManagerThe "Data Cloud Admin" permission set grants access to manage models in Data Cloud (assumed as Einstein Studio's context), including viewing and editing AI models. "Prompt Template Manager" isn't a real set, but Prompt Builder creation is covered by "Einstein Generative AI Admin" or similar admin-level access (assumed intent).
This combination approximates the needs, making it the closest correct answer despite naming ambiguity.
* Option C: Prompt Template User and Data Cloud Admin"Prompt Template User" isn't a standard set, and user-level access (e.g., Einstein Generative AI User) typically allows execution, not creation.
The data scientist needs to create templates, so this lacks sufficient Prompt Builder rights, making it incorrect.
Why Option B is Correct (with Caveat):
"Data Cloud Admin" covers model management in Data Cloud (likely intended as Einstein Studio), and
"Prompt Template Manager" is interpreted as admin-level Prompt Builder access (e.g., Einstein Generative AI Admin). Despite naming inconsistencies, this fits the requirements per Salesforce permissions structure.
References:
Salesforce Data Cloud Documentation: Permissions- Details Data Cloud Admin for models.
Trailhead: Set Up Einstein Generative AI- Covers Prompt Builder admin access.
Salesforce Help: Agentforce Permission Sets- Aligns with admin-level needs.
NEW QUESTION # 33
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production.
Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?
Answer: C
Explanation:
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.
ConclusionThe 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 References & Documents
Salesforce Trailhead: Work Summaries with Einstein GPTProvides an overview of how to configure Work Summaries, including the need for custom fields, quick actions, and UI components.
Salesforce Documentation: Deploying Metadata Between OrgsExplains 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 GuideOutlines which Einstein Generative AI and Work Summaries configurations are deployable as metadata.
NEW QUESTION # 34
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 theAgentforce Specialistinclude these instructions on a new line in the prompt template?
Answer: B
Explanation:
In Salesforce prompt templates, instructions that guide how the Large Language Model (LLM) should generate content (in this case, personalized emails) can be included by surrounding the instruction text with triple quotes ("""). This formatting ensures that the LLM adheres to the specific instructions while generating the email content.
The use oftriple quotesallows the AI to understand that the enclosed text is a directive for how to approach the task, such as limiting the content to associated opportunity information or encouraging a specific action from the recipient.
Refer toSalesforce Prompt Builder documentationfor detailed instructions on how to structure prompts for generative AI.
NEW QUESTION # 35
What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation:In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions. Let's evaluate its role.
* Option A: Find similar requested topics and provide the actions that need to be executed.While the LLM can identify similar topics, its role extends beyond merely finding them-it matches intents to specific topics and determines execution. This option understates the LLM's responsibility for ordering actions, making it incomplete and incorrect.
* Option B: Identify the best matching topic and actions and correct order of execution.The LLM analyzes user input to understand intent, matches it to the best-fitting topic (configured in Agent Builder), and selects associated actions. It also determines the correct sequence of execution based on the agent's plan (e.g., retrieve data before updating a record). This end-to-end process-from intent recognition to action orchestration-is the LLM's core role in Agentforce, making this the correct answer.
* Option C: Determine a user's topic access and sort actions by priority to be executed.Topic access is governed by Salesforce permissions (e.g., user profiles), not the LLM. While the LLM prioritizes actions within its plan, its primary role is intent matching and executionordering, not access control, making this incorrect.
Why Option B is Correct:The LLM's role in identifying topics, selecting actions, and ordering execution is central to Agentforce's autonomous functionality, as detailed in Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Atlas Reasoning Engine- Outlines LLM's intent and action handling.
* Trailhead: Understand Agentforce Technology- Explains topic matching and execution.
* Salesforce Help: Agentforce Actions- Confirms LLM's role in orchestrating responses.
NEW QUESTION # 36
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