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Salesforce Certified Agentforce Specialist (AI-201) Exam Questions
- Topic 1: AI Agents: This domain covers configuring agent behavior, understanding the reasoning engine, selecting topics and actions for agent types, managing Agent User security, choosing appropriate agent types, and connecting agents to various channels.
- Topic 2: Prompt Engineering: This section focuses on using Prompt Builder, managing user roles, creating prompt templates with field generation and flex types, selecting grounding techniques, and applying best practices for effective prompts.
- Topic 3: Data Cloud for Agentforce: This domain covers Agentforce Data Library types, improving responses with unstructured data through chunking and indexing, understanding retrievers, and selecting keyword, vector, or hybrid search types.
- Topic 4: Development Lifecycle: This area addresses testing agents in Testing Center, deploying from sandbox to production, and managing agent adoption and monitoring.
- Topic 5: Multi-Agent Interoperability: This domain explains Model Context Protocol (MCP), agent-to-agent communication, and when to use Agent API for system interactions.
Free Salesforce Salesforce Certified Agentforce Specialist (AI-201) Exam Actual Questions
Note: Premium Questions for Salesforce Certified Agentforce Specialist (AI-201) were last updated On Jun. 17, 2026 (see below)
What is the primary function of the planner service in the Agent system?
The primary function of the planner service in the Agent system is to identify copilot actions that should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that the Agent should perform based on user input.
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the
content's safety level.
What does a safety category score of 1 indicate in the Einstein Generative Toxicity Score?
In the Einstein Trust Layer, the toxicity scoring system is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1 indicates that the content is deemed safe.
The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by the Einstein Trust Layer.
For further reference, check Salesforce's official Einstein Trust Layer documentation regarding toxicity scoring for AI-generated content.
In a Knowledge-based data library configuration, what is the primary difference between the identifying fields and the content fields?
In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses. Let's analyze their roles.
Option A: Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.
In a Knowledge-based data library, identifying fields (e.g., Title, Article Number, or custom metadata) are used to search and pinpoint the relevant Knowledge article based on user input or context. Content fields (e.g., Article Body, Details) provide the substantive data that the AI uses to generate detailed, enriched responses. This distinction is critical for grounding Agentforce prompts and aligns with Salesforce's documentation on Knowledge integration, making it the correct answer.
Option B: Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.
Identifying fields do more than categorize---they actively locate articles, not just index them. Content fields aren't limited to summaries; they include full article content for response generation, not just display. This option underrepresents their roles and is incorrect.
Option C: Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.
While identifying fields contribute to relevance (e.g., via search terms), their primary role is locating articles, not just scoring. Content fields do store full text, but their purpose is to enrich responses, not merely enable retrieval. This option shifts focus inaccurately, making it incorrect.
Why Option A is Correct:
The primary difference---identifying fields for locating articles and content fields for enriching responses---reflects their roles in Knowledge-based grounding, as per official Agentforce documentation.
Salesforce Agentforce Documentation: Grounding with Knowledge > Data Library Setup -- Defines identifying vs. content fields.
Trailhead: Ground Your Agentforce Prompts -- Explains field roles in Knowledge integration.
Salesforce Help: Knowledge in Agentforce -- Confirms locating and enriching functions.
Universal Containers is considering leveraging the Einstein Trust Layer in conjunction with Einstein Generative AI Audit Data.
Which audit data is available using the Einstein Trust Layer?
Universal Containers is considering the use of the Einstein Trust Layer along with Einstein Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to use AI by offering features like data masking and toxicity assessment.
The audit data available through the Einstein Trust Layer includes information about masked data---which ensures sensitive information is not exposed---and the toxicity score, which evaluates the generated content for inappropriate or harmful language.
Salesforce Agentforce Specialist Documentation - Einstein Trust Layer: Details the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.
The Agentforce Specialist for Cloud Kicks wants to create an agent that will allow the sales staff to schedule their daily tasks and assist in providing detailed explanations behind product prices and deals.
Following Salesforce best practices, which type of agent should they create?
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
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