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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Agents	35%	- Agent behavior configuration 1. Filters and variables for deterministic behavior 2. Agent reasoning engine concepts - Agent setup and orchestration 1. Standard and custom agent actions 2. Standard and custom topics
Topic 2: Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior
Topic 3: Prompt Engineering	20%	- Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations - Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration
Topic 4: Data Cloud & Grounding	20%	- Data integration for AI agents 1. Connecting external data sources 2. Using Data Cloud for retrieval augmentation
Topic 5: Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Sandbox to production deployment 2. Testing and validation strategies

>> Sample AI-201 Questions <<

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Salesforce Certified Agentforce Specialist Sample Questions (Q24-Q29):

NEW QUESTION # 24
Universal Containers (UC) is preparing and defining success criteria for Agentforce Testing Center test cases. Which details should UC specify as the expected output to ensure the tests accurately reflect the agent's functionality?

A. Expected Topic API Name
B. Expected Flow API Name
C. Expected Prompt Template Name

Answer: A

Explanation:
According to the AgentForce Testing Center Reference Guide, each test case in the Testing Center should define a clear expected output to validate that the agent selects and executes the correct topic in response to a given user utterance.
The Expected Topic API Name acts as the validation reference -- it ensures that the reasoning engine correctly classifies the user's intent and routes the conversation to the appropriate topic.
This allows the test to confirm end-to-end functionality, from intent detection to action execution.

NEW QUESTION # 25
Universal Containers' service team wants to customize the standard case summary response from Agentforce. What should the Agentforce Specialist do to achieve this?

A. Summarize the Case with a standard Agent action.
B. Create a custom Record Summary prompt template for the Case object.
C. Customize the standard Record Summary template for the Case object.

Answer: B

Explanation:
UC's service team seeks to customize the standard case summary response provided by Agentforce.

NEW QUESTION # 26
Universal Containers deploys a new Agentforce Service Agent into the company's website but is getting feedback that the Agentforce Service Agent is not providing answers to customer questions that are found in the company's Salesforce Knowledge articles. What is the likely issue?

A. The Agentforce Service Agent user is not assigned the correct Agent Type License.
B. The Agentforce Service Agent user needs to be created under the standard Agent Knowledge profile.
C. The Agentforce Service Agent user was not given the Allow View Knowledge permission set.

Answer: C

Explanation:
Universal Containers (UC) has deployed an Agentforce Service Agent on its website, but it's failing to provide answers from Salesforce Knowledge articles.

NEW QUESTION # 27
Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management. For security purposes, UC wants the agent to verify a customer's identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed. Which approach best meets this requirement?

A. Create a variable to store the verification status, set it as output from a "Verify Identity" action, and apply a filter so any case-related actions only run when the variable confirms verification.
B. Store the verification status in a custom variable and set a global instruction that the agent should check this variable before sharing case information.
C. Use keywords such as "Always" and "Never" to write clear logic in Topic Instructions to verify user identity before providing any case information,

Answer: A

Explanation:
The AgentForce for Service Implementation Guide clearly outlines that when an agent must verify identity before performing any case-related operations, the correct method is to use a variable- based control flow. The documentation specifies: "To maintain deterministic and secure behavior, define a variable (for example, `isVerified') that stores the result of an identity verification step.
Use this variable as a conditional filter in the topic flow to ensure that case-related actions execute only when the variable equals `true'." This ensures that no sensitive or case-specific data is shared unless verification is explicitly confirmed. It provides a deterministic safeguard, as the system only proceeds with case data actions after the verification variable confirms completion.

NEW QUESTION # 28
Universal Containers built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors. What is the cause of the random nature of this error?

A. The number of tokens generated by the dynamic nature of the prompt template will vary by record.
B. The number of tokens that can be processed by the LLM varies with total user demand.
C. The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.

Answer: A

Explanation:
In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM's token capacity (e.g., input + output tokens). The random nature of these failures suggests variability in the token count across different records, which is directly addressed by Option B.
Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record-some records might have short text fields while others have lengthy ones-the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM's limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.

NEW QUESTION # 29
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