[2026] New AI-201 exam dumps Use Updated Salesforce Exam
Verified AI-201 Dumps Q&As - AI-201 Test Engine with Correct Answers
NEW QUESTION # 151
Coral Cloud Resorts wants visibility into credit usage associated with testing. Which feature supports this?
- A. Agentforce Analytics
- B. Digital Wallet
- C. Testing Center
Answer: C
Explanation:
The AgentForce Testing Center Guide specifies that all testing activity--including prompt tests, structured batch runs, and evaluation sessions--consumes AI credits, which can be monitored directly through the Testing Center interface. The documentation states: "Testing Center provides visibility into testing performance, outcomes, and credit usage. Administrators can view test credit consumption metrics to understand resource utilization across agents and environments."
NEW QUESTION # 152
Universal Containers tests out a new Einstein Generative AI feature for its sales team to create personalized and contextualized emails for its customers. Sometimes, users find that the draft email contains placeholders for attributes that could have been derived from the recipient's contact record. What is the most likely explanation for why the draft email shows these placeholders?
- A. The user's locale language is not supported by Prompt Builder.
- B. The user does not have Einstein Sales Emails permission assigned.
- C. The user does not have permission to access the fields.
Answer: C
Explanation:
UC is using an Einstein Generative AI feature (likely Einstein Sales Emails) to draft personalized emails, but placeholders (e.g., {!Contact.FirstName}) appear instead of actual data from the contact record.
NEW QUESTION # 153
An agent incorrectly updates records when given ambiguous user instructions. What is the BEST mitigation strategy?
- A. Increase the number of skills
- B. Remove update permissions
- C. Disable automation
- D. Improve prompt instructions and constraints
Answer: D
Explanation:
Clear prompt instructions, guardrails, and constraints reduce unintended actions caused by ambiguity.
NEW QUESTION # 154
Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding. What should UC consider when using standard Service AI Grounding?
- A. Service AI Grounding only supports String and Text Area type fields.
- B. Service AI Grounding only works with Case and Knowledge objects.
- C. Service AI Grounding visibility works m system mode.
Answer: A
Explanation:
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses.
Key considerations:
NEW QUESTION # 155
An Agentforce Specialist builds a new Service Agent that uses a custom action built on a flow.
The agent has been tested in a sandbox and is now ready to deploy. What is a key consideration regarding the activation status of the agent in the production environment?
- A. The agent will automatically be activated upon successful deployment.
- B. The agent will be activated automatically only if the flow is also active.
- C. The agent must be manually activated in production, regardless of its status in the sandbox.
Answer: C
Explanation:
According to the AgentForce Deployment and Lifecycle Management Guide, when an agent is deployed from a sandbox to a production environment, activation does not carry over automatically. The documentation clarifies: "Each environment maintains its own activation state.
Agents must be manually activated in production after deployment to ensure controlled rollout and compliance validation."
NEW QUESTION # 156
Which statement explains why a company might prefer a hybrid search index in Data Cloud for Agentforce?
- A. Hybrid search indexes support both literal keyword matches and semantic recall, useful when queries mix specific terms and intent.
- B. Hybrid search indexes process queries faster than vector search because they eliminate the need for semantic embedding.
- C. Vector embedding in hybrid search are prefiltered by keyword matches, reducing computational overhead and improving response accuracy.
Answer: A
Explanation:
According to the AgentForce Data Cloud Search Indexing Guide and RAG Optimization Framework, a hybrid search index combines both keyword-based (lexical) and vector-based (semantic) search capabilities. This dual-mode retrieval enables AgentForce to interpret user intent while still honoring exact keyword matches.
In many enterprise scenarios, queries contain a mixture of specific terms (e.g., "contract ID
54321") and semantic intent (e.g., "renew my subscription"). A purely vector search might overlook exact keywords, while a keyword-only search might miss semantically relevant results.
Hybrid indexing ensures that both types of retrieval are available simultaneously - providing the best balance of precision and contextual understanding.
NEW QUESTION # 157
Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature. What does Einstein consider when generating a summary?
- A. Generation is grounded with conversation context and Knowledge articles.
- B. Generation is grounded with conversation context, Knowledge articles, and cases.
- C. Generation is grounded with existing conversation context only.
Answer: B
Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
Conversation Context:
Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
Knowledge Articles:
It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
Cases:
Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
NEW QUESTION # 158
Universal Containers has a custom Agent action calling a flow to retrieve the real-time status of an order from the order fulfillment system. For the given flow, what should the Agentforce Specialist consider about the running user's data access?
- A. The custom action adheres to the permissions, held-level security, and sharing settings configured in the flow.
- B. The flow must have the "with sharing" permission selected m the advanced settings for the permissions, field-level security, and sharing settings to be respected.
- C. The Agent will always run flows in system mode so the running user's data access will not affect the data returned.
Answer: A
Explanation:
When a flow is invoked via a custom Agent action, its data access depends on the flow's runtime configuration, not system mode by default. Salesforce flows can be configured to respect the running user's permissions and sharing settings:
If the flow is set to "Run as the User Who Launched the Flow" (enabled in Flow Settings), it adheres to the user's permissions, field-level security (FLS), and sharing rules.
NEW QUESTION # 159
What is the main benefit of using a Knowledge article in an Agentforce Data Library?
- A. It provides a structured, searchable repository of approved documents so the agent can retrieve reliable information for each inquiry..
- B. The retriever for Knowledge articles has better accuracy and performance than the default retriever.
- C. Only the retriever for Knowledge articles allows for agents to access Knowledge from both inside the platform and on a customer's website.
Answer: A
Explanation:
Why is "A structured, searchable repository of approved documents" the correct answer?
Using a Knowledge Article in an Agentforce Data Library ensures that agents can quickly access reliable and pre-approved information during customer interactions.
Key Benefits of Knowledge Articles in an Agentforce Data Library:
Ensures Information Accuracy and Consistency
Knowledge articles provide approved, well-structured responses, reducing the risk of misinformation.
This ensures customer service consistency across different agents.
Improves Searchability and AI-Grounded Responses
Articles are indexed and retrieved efficiently by AI-powered search engines.
AI-generated responses are grounded in accurate, structured knowledge, improving response quality.
Enhances Customer Support and Agent Productivity
Agents spend less time searching for information and more time resolving customer inquiries.
Einstein AI can suggest the most relevant articles based on conversation context.
NEW QUESTION # 160
Universal Containers (UC) wants to build an Agentforce Service Agent that provides the latest, active, and relevant policy and compliance information to customers. The agent must:
- Semantically search HR policies, compliance guidelines, and company
procedures.
- Ensure responses are grounded on published Knowledge.
- Allow Knowledge updates to be reflected immediately without manual
reconfiguration.
What should UC do to ensure the agent retrieves the right information?
- A. Manually add policy responses into the AI model to prevent hallucinations.
- B. Set up an Agentforce Data Library to store and index policy documents for AI retrieval.
- C. Enable the agent to search all internal records and past customer inquiries.
Answer: B
Explanation:
UC requires an Agentforce Service Agent to deliver accurate, up-to-date policy and compliance info with specific criteria.
NEW QUESTION # 161
What is the correct process to leverage Prompt Builder in a Salesforce org?
- A. Select the appropriate prompt template type to use, select one of Salesforce's standard prompts, determine the object to associate the prompt, select a record to validate against, and associate the prompt to an action.
- B. Enable the target object for generative prompting, develop the prompt within the prompt workspace, select records to fine-tune and ground the response, enable the Trust Layer, and associate the prompt to an action.
- C. Select the appropriate prompt template type to use, develop the prompt within the prompt workspace, select resources to dynamically insert CRM-derived grounding data, pick the model to use, and test and validate the generated responses.
Answer: C
Explanation:
When using Prompt Builder in a Salesforce org, the correct process involves several important steps:
Select the appropriate prompt template type based on the use case.
Develop the prompt within the prompt workspace, where the template is created and customized.
Select CRM-derived grounding data to be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.
Pick the model to use for generating responses, either using Salesforce's built-in models or custom ones.
Test and validate the generated responses to ensure accuracy and effectiveness.
NEW QUESTION # 162
Universal Containers (UC) stores case details and updates in several custom fields and custom objects related to the case. UC would like its Agentforce Service Agent to be able to provide information in these fields and related records as part of an answer back to its customers when the customer is asking for updates. Which best practice should UC follow to grant access to this information for the Agentforce Service Agent?
- A. Update the Object and Field access in the Einstein Agent User Profile so that the Agentforce Service Agents will always get the necessary access.
- B. Create a new permission set with the Einstein Agent License and enable Read access to the custom fields and custom objects, and assign it to the Agentforce Service Agent user.
- C. Update the Object and Field access in the AgentforceServiceAgentUserPsg permission set group that is already assigned to the Agentforce Service Agent user,
Answer: C
Explanation:
Per the AgentForce Security and Permission Management Guide, the
AgentForceServiceAgentUserPsg (Permission Set Group) controls access privileges for Service Agents, including which Salesforce objects, fields, and related data they can read or interact with.
When extending an agent's access to additional custom fields or related objects, the documented best practice is to update the existing permission set group assigned to that agent type rather than creating new or profile-based permissions. This approach maintains centralized permission governance, ensures license alignment, and avoids conflicts or redundancy across multiple permission layers.
NEW QUESTION # 163
Universal Containers (UC) has a library of custom-built personalized investment portfolio APIs, and is planning to extend it to agents. Which method should UC's agent choose to dynamically use the best API service?
- A. MuleSoft connector for custom hosted processes
- B. Model Context Protocol (MCP) server support
- C. Agent-to-Agent (A2A) protocol support
Answer: B
Explanation:
The most appropriate and advanced method for an Agentforce agent to dynamically select and use the best API service from a library of custom-built APIs is through Model Context Protocol (MCP) server support (B).
The Model Context Protocol (MCP) is an open standard specifically designed to standardize how AI agents and Large Language Models (LLMs) interact with external tools, systems, and data sources (like custom APIs). An external system, such as a server hosting UC's custom portfolio APIs, can be exposed as an MCP Server. This server provides rich, standardized, human- readable metadata about its "tools" (the APIs it offers). The Agentforce Atlas Reasoning Engine can interpret this metadata to understand the function of each API, the required inputs, and the expected outputs. This allows the agent to dynamically discover, reason over, and select the most appropriate API to execute based on a user's request (e.g., "Show me the best-performing portfolio" vs. "Adjust my risk tolerance").
NEW QUESTION # 164
Which element in the Omni-Channel Flow should be used to connect the flow with the agent?
- A. Decision
- B. Route Work Action
- C. Assignment
Answer: B
Explanation:
UC is integrating an Agentforce agent with Omni-Channel Flow to route work.
NEW QUESTION # 165
Cloud Kicks (CK) recently finished the development of a new prompt template that uses its own large language model (LLM). CK is deploying a prompt template from a sandbox to a production org, and is receiving an error. When trying to deploy the change set, CK is getting an error related to the LLM used in the prompt template. What is the cause of the error?
- A. BYOLLM is not yet supported for in prompt templates in production.
- B. The name of the LLM does not match in sandbox and production.
- C. The prompt does not specify that it is a custom LLM.
Answer: B
Explanation:
As documented in the AgentForce Prompt Template and BYOLLM (Bring-Your-Own-LLM) Deployment Guide, each prompt template references a specific LLM configuration by name and ID. When migrating components between environments (e.g., from sandbox to production), the referenced LLM must also exist in the target org with the exact same name and identifier.
If the LLM configuration is missing or named differently in production, the deployment fails, as the prompt template cannot resolve its model dependency.
NEW QUESTION # 166
Universal Containers has multiple Salesforce orgs, each with a unique customer service agent where a verification agent must pass customer identity data to downstream agents handling account modifications. The customer ID must remain secure and persistent across agent handoffs without exposure to large language model (LLM) modification. What is the most appropriate configuration?
- A. Use the Agent API to start the downstream agent's session and pass the verified customer ID as a read-only context variable, ensuring security and preventing LLM alteration.
- B. Store customer identity information in conversation variables created by the first agent and have other agents read those same conversation variables.
- C. Implement a custom object to temporarily store verification status and have each agent query it via SOQL actions during execution.
Answer: A
Explanation:
The AgentForce Inter-Agent Communication and Security Configuration Guide specifies that when sensitive identity data (like a verified customer ID) must be shared between agents, the correct approach is to use the Agent API to initiate the downstream agent's session. The verified data should be passed as a read-only context variable, ensuring persistence across sessions while preventing modification by the large language model (LLM).
This setup maintains data integrity and security compliance by isolating sensitive variables from the LLM's reasoning layer. Context variables passed via the Agent API are immutable during runtime, ensuring they cannot be altered or exposed in agent-generated responses.
NEW QUESTION # 167
What considerations should an Agentforce Specialist be aware of when using Record Snapshots grounding in a prompt template?
- A. Email addresses associated with the object are excluded.
- B. Activities such as tasks and events are excluded.
- C. Empty data, such as fields without values or sections without limits, is filtered out.
Answer: B
Explanation:
Record Snapshots grounding in Agentforce prompt templates allows the AI to access and use data from a specific Salesforce record (e.g., fields and related records) to generate contextually relevant responses. However, there are specific limitations to consider. Let's analyze each option based on official documentation.
NEW QUESTION # 168
Universal Containers has a requirement to provide a sales summary for its sales reps who are using Employee Agents, but they are not happy with the default answer. Which best practice should the AgentForce Specialist recommend?
- A. Create a Knowledge Answer custom prompt template.
- B. Update the standard record summary action.
- C. Create a Record Summary custom prompt template.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Prompt Customization and Template Development Guide, when an organization wants to modify or enhance how structured data (like Salesforce records) is summarized or presented by an agent, the correct approach is to create a Record Summary custom prompt template.
Record Summary templates allow specialists to tailor how the agent interprets and communicates record information - such as opportunities, accounts, or sales summaries - ensuring the output aligns with company-specific terminology, tone, and data needs.
NEW QUESTION # 169
Universal Containers is very concerned about security compliance and wants to understand:
- Which prompt text is sent to the large language model (LLM)
- How it is masked
- The masked response
What should the Agentforce Specialist recommend?
- A. Enable audit trail in the Einstein Trust Layer.
- B. Ingest the Einstein Shield Event logs into CRM Analytics.
- C. Review the debug logs of the running user.
Answer: A
Explanation:
To address security compliance concerns and provide visibility into the prompt text sent to the LLM, how it is masked, and the masked response, the Agentforce Specialist should recommend enabling the audit trail in the Einstein Trust Layer. This feature captures and logs the prompts sent to the large language model (LLM) along with the masking of sensitive information and the AI's response. This audit trail ensures full transparency and compliance with security requirements.
NEW QUESTION # 170
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