Salesforce Certified Agentforce Specialist (AI-201) Spring 26 Update Dumps July 2026
PDF + Test Engine
$105 $74
Test Engine
$85 $60
PDF
$65 $46
Here are Salesforce Agentforce-Specialist PDF available features:
379 questions with answers
Updation Date : 16 Jul, 2026
1 day study required to pass exam
100% Passing Assurance
100% Money Back Guarantee
Free 3 Months Updates
Salesforce Agentforce-Specialist Sample Questions
Question # 91
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 flow must have the "with sharing" permission selected m the advanced settings for the permissions, field-level security, and sharing settings to be respected. B. The custom action adheres to the permissions, held-level security, and sharing settings configured in the flow. C. The Agent will always run flows in system mode so the running user's data access will not affect the data returned.
Answer: B Explanation: When a flow is invoked via a custom Agent action, its data access depends on the flow’s run time 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. Option C is incorrect because flows do not always run in system mode unless explicitly configured to do so. Option A is misleading because "with sharing" is an Apex concept, not a flow setting.Flows use runtime settings like FLS and sharing enforcement. References: Salesforce Help: Flow Runtime and Security Context Flow Settings: "Run with User Permission and Field-Level Security" ensures data Access aligns with the user’s permissions
Question # 92
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?
A. Response accuracy and offensiveness score B. Hallucination score and bias score C. Masked data and toxicity score
Answer: C Explanation: 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.References: SalesforceAgentforce SpecialistDocumentation - Einstein Trust Layer:Details the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.
Question # 93
An Agentforce created a custom Agent action, but it is not being picked up by the planner service in the
correct order. Which adjustment should the Al Specialist make in the custom Agent action instructions for the planner
service to work as expected?
A. Specify the dependent actions with the reference to the action API name. B. Specify the profiles or custom permissions allowed to invoke the action. C. Specify the LLM model provider and version to be used to invoke the action.
Answer: A Explanation: When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, theAgentforce Specialistmust explicitly specify dependent actions using their API names in the custom action’s configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements. Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A’s API name must be listed as a dependency in Action B’s instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead torace conditions or incorrect execution order. In contrast:Profiles or custom permissions (B) control access to the action but do not influence execution order. LLM model provider and version (C) determine the AI model used for processing but are unrelated to the planner’s sequencing logic.
Reference: Salesforce Help Article: Configure Custom Actions for Einstein Bots (Section: "Defining Action Dependencies"). Einstein Bot Developer Guide: "Orchestrating Workflows with the Planner Service" (Dependency Management best practices).
Question # 94
Universal Containers (UC) is looking to enhance its operational efficiency. UC has recently adopted
Salesforce and is considering implementing Einstein Copilot to improve its processes. What is a key reason for implementing Einstein Copilot?
A. Improving data entry and data cleansing B. Allowing AI to perform tasks without user interaction C. Streamlining workflows and automating repetitive tasks
Answer: C Explanation: The key reason for implementingEinstein Copilotis its ability tostreamline workflowsandautomate repetitive tasks. By leveraging AI, Einstein Copilot can assist users in handling mundane, repetitive processes, such as automatically generating insights, completing actions, and guiding users through complex processes, all of which significantly improve operational efficiency. Option A(Improving data entry and cleansing) is not the primary purpose of Einstein Copilot, as its focus is on guiding and assisting users through workflows. Option B(Allowing AI to perform tasks without user interaction) does not accurately describe the role of Einstein Copilot, which operates interactively to assist users in real time. SalesforceAgentforce SpecialistReferences:More details can be found in the Salesforce documentation:https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_overview.html
Question # 95
An Agentforce is creating a custom action in Einstein Copilot. Which option is available for the Agentforce Specialist to choose for the custom copilot action?
A. Apex trigger B. SOQL C. Flows
Answer: C Explanation: When creating a custom action in Einstein Copilot, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the Agentforce Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot's functionality without needing custom code. While Apex trigger sand SOQL are important Salesforce tools,Flows are the recommended method for creating custom actions within Einstein Copilot because they are declarative and highly adaptable. For further guidance, refer to Salesforce Flow documentation and Einstein Copilot customization resources.
Question # 96
What is best practice when refining Einstein Copilot custom action instructions?
A. Provide examples of user messages that are expected to trigger the action. B. Use consistent introductory phrases and verbs across multiple action instructions. C. Specify the persona who will request the action.
Answer: A Explanation: When refining Einstein Copilot custom action instructions, it is considered best practice toprovide examples of user messages that are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages. Option B(consistent phrases) can improve clarity but does not directly refine the triggering logic. Option C(specifying a persona) is not as crucial as giving examples that illustrate how users will interact with the custom action.For more details, refer to Salesforce's Einstein Copilot documentation on building and refining custom actions.
Question # 97
Universal Containers implemented Einstein Copilot for its users.
One user complains that Einstein Copilot is not deleting activities from the past 7 days. What is the reason for this issue?
A. Einstein Copilot Delete Record Action permission is not associated to the user. B. Einstein Copilot does not have the permission to delete the user's records. C. Einstein Copilot does not support the Delete Record action.
Answer: C Explanation: Einstein Copilot currently supports various actions like creating and updating records but does not support the Delete Record action. Therefore, the user's request to delete activities from the past 7 days cannot be fulfilled using Einstein Copilot. Unsupported Action:The inability to delete records is due to the current limitations of Einstein Copilot's supported actions. It is designed to assist with tasks like data retrieval, creation, and updates, but for security and data integrity reasons, it does not facilitate the deletion of records. User Permissions:Even if the user has the necessary permissions to delete records within Salesforce, Einstein Copilot itself does not have the capability to execute delete operations.
Question # 98
Universal Containers (UC) plans to automatically populate the Description field on the Account object.
Which type of prompt template should UC use?
A. Field Generation prompt template B. Flex Prompt template C. Sales Email prompt template
Answer: A Explanation: Context of the Question Universal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field. Field Generation Prompt Template Why Not Flex or Sales Email Prompt Templates? Conclusion For automatically populating the Description field with AI-generated content, the Field Generation prompt template(Option A) is the correct choice.Salesforce Agentforce Specialist References & Documents Salesforce Documentation:Prompt Template Types Explains various template types (Field Generation, Flex, Email, etc.) and their typical use cases. Salesforce Agentforce Specialist Study Guide Highlights Field Generation prompt templates for populating or updating record fields with AI-generated text.
Question # 99
When a customer chat is initiated, which functionality in Salesforce provides generative AI replies or draft
emails based on recommended Knowledge articles?
A. Einstein Reply Recommendations B. Einstein Service Replies C. Einstein Grounding
Answer: B Explanation: When a customer chat is initiated,Einstein Service Replies provides generative AI replies or draft emails based on recommended Knowledge articles. Thisfeature uses the information from the Salesforce Knowledge base to generate responses that are relevant to the customer's query, improving the efficiency and accuracy of customer support interactions. Option B is correct because Einstein Service Replies is responsible for generating AI-driven responses based on knowledge articles. Option A (Einstein Reply Recommendations) is focused on recommending replies but does not generate them. Option C (Einstein Grounding) refers to grounding responses in data but is not directly related to drafting replies. References: Einstein Service Replies Overview:https://help.salesforce.com/s/articleView?id=sf.einstein_service_replies.htm
Question # 100
Universal Containers (UC) is tracking web activities in Data Cloud for a unified contact, and wants to use that
in a prompt template to help extract insights from the data.
Assuming that the Contact object is one of the objects associated with the prompt template, what is a valid
way for DC to do this?
A. Call the prompt directly from Data Cloud with a web tracing activity included in the prompt definition. B. Add the activity records as an enrichment related list to the Contact then pass the Contact into a prompt template workspace using related list grounding. C. Create a prompt template that takes a list of all Data Cloud activity records as input to pass to the large language model (LLM).
Answer: B
? Explanation
This question is about how to bring web activity data into prompt templates for an LLM to analyze — a key use case in the Einstein 1 Platform (Agentforce). Let’s break it down:
In Data Cloud, web activity (like page views, clicks, etc.) is typically stored as activity objects linked to the Contact via a unified profile ID.
When designing a prompt template, you often pass a CRM or Data Cloud object as context, such as a Contact record. However, the LLM only “sees” fields you explicitly provide.
To include related activity data (like web activity) in the prompt, Salesforce provides related list grounding:
You define an enrichment related list on the Contact object that pulls in related activity records.
When the prompt is executed, the enrichment automatically provides the list of web activities to the prompt as context for the LLM.
Therefore, Option B is the valid and correct way for UC to include web activity in the prompt template.
Option A is incorrect because while you can call prompts from Data Cloud, there’s no way to “embed” a web tracing activity directly into the prompt definition without first associating it via objects or related lists.
Option C is incorrect because:
Passing a list of all Data Cloud activity records into a prompt is not practical or efficient; it would involve massive amounts of data and unrelated activities.
Prompts are designed to work with records tied to specific objects (like Contact) and their related lists, not the entire Data Cloud.
