[Jul-2026] Exam Sure Pass Salesforce Certification with AI-201 exam questions
Real Salesforce AI-201 Exam Questions Study Guide
NEW QUESTION # 123
Universal Containers (UC) wants to improve the efficiency of addressing customer questions and reduce agent handling time with AI- generated responses. The agents should be able to leverage their existing knowledge base and identify whether the responses are coming from the large language model (LLM) or from Salesforce Knowledge. Which step should UC take to meet this requirement?
- A. Turn on Service AI Grounding, Grounding with Case, and Service Replies.
- B. Turn on Service AI Grounding and Grounding with Knowledge.
- C. Turn on Service Replies, Service AI Grounding, and Grounding with Knowledge.
Answer: B
Explanation:
To meet Universal Containers' goal of improving efficiency and reducing agent handling time with AI-generated responses, the best approach is to enable Service Replies, Service AI Grounding, and Grounding with Knowledge.
Service Replies generates responses automatically.
Service AI Grounding ensures that the AI is using relevant case data.
Grounding with Knowledge ensures that responses are backed by Salesforce Knowledge articles, allowing agents to identify whether a response is coming from the LLM or Salesforce Knowledge.
NEW QUESTION # 124
Universal Containers (UC) is discussing its AI strategy in an agile Scrum meeting. Which business requirement would lead An Agentforce to recommend connecting to an external foundational model via Einstein Studio (Model Builder)?
- A. UC wants a model fine-tuned using company data.
- B. UC wants to change the frequency penalty of the model.
- C. UC wants to fine-tune model temperature.
Answer: A
Explanation:
Einstein Studio (Model Builder) allows organizations to connect and utilize external foundational models while fine-tuning them with company-specific data. This capability is particularly suited to businesses like Universal Containers (UC) that require customization of foundational models to better align with their unique data and use cases.
NEW QUESTION # 125
Universal Containers (UC) has configured a data library and wants to restrict indexing of knowledge articles to articles which are only publicly available in their knowledge base, UC also wants the agent to link sources that the large language model (LLM) grounded its response on.
Which settings should help UC with this?
- A. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article. It is not possible to display articles that the LLM grounded its response in.
- B. Use Data Categories to categorize publicly available articles to index. Sources are automatically displayed when knowledge articles are categorized as Public.
- C. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article and select Show sources,
Answer: C
Explanation:
According to the AgentForce Data Library Configuration Guide, administrators can restrict indexing and retrieval of Knowledge articles to publicly available ones and enable source visibility for LLM-grounded responses. The documentation states:
"Within the data library settings, under Knowledge Settings, enable `Use Public Knowledge Articles' to ensure only publicly visible content is indexed. To display citations, enable `Show Sources' so the agent links the specific articles or data records used to ground its response."
NEW QUESTION # 126
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?
- A. Secure Data Retrieval and Grounding
- B. Prompt Defense
- C. Data Masking
Answer: B
Explanation:
The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.
Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.
NEW QUESTION # 127
Universal Containers has seen a high adoption rate of a new feature that uses generative AI to populate a summary field of a custom object, Competitor Analysis. All sales users have the same profile but one user cannot see the generative AlI-enabled field icon next to the summary field.
What is the most likely cause of the issue?
- A. The prompt template associated with summary field is not activated for that user.
- B. The user does not have the field Generative AI User permission set assigned.
- C. The user does not have the Prompt Template User permission set assigned.
Answer: B
Explanation:
In Salesforce, Generative AI capabilities are controlled by specific permission sets. To use features such as generating summaries with AI, users need to have the correct permission sets that allow access to these functionalities.
Generative AI User Permission Set: This is a key permission set required to enable the generative AI capabilities for a user. In this case, the missing Generative AI User permission set prevents the user from seeing the generative AI-enabled field icon. Without this permission, the generative AI feature in the Competitor Analysis custom object won't be accessible.
NEW QUESTION # 128
Why is starting with a narrowly scoped Agentforce use case considered a best practice?
- A. It limits risk and simplifies monitoring
- B. It eliminates user training
- C. It reduces Salesforce license costs
- D. It avoids the need for prompts
Answer: A
Explanation:
A narrow scope limits unintended behavior and makes monitoring, tuning, and governance easier.
NEW QUESTION # 129
An agent must comply with regional data regulations (e.g., GDPR). Which Salesforce capability MOST directly supports this?
- A. Lightning App Builder
- B. Einstein Trust Layer governance controls
- C. Reports and Dashboards
- D. Custom metadata types
Answer: B
Explanation:
The Einstein Trust Layer enforces data residency, masking, and compliance policies.
NEW QUESTION # 130
Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions. Which use case functionality of Model Builder aligns with UC's request?
- A. Contract Renewal Date prediction
- B. Customer churn prediction
- C. Product recommendation prediction
Answer: B
Explanation:
Customer churn prediction is the best use case for Model Builder in addressing Universal Containers' concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn, UC can proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.
NEW QUESTION # 131
A sales rep at Universal Containers is extremely busy and sometimes will have very long sales calls on voice and video calls and might miss key details. They are just starting to adopt new generative AI features. Which Einstein Generative AI feature should An Agentforce recommend to help the rep get the details they might have missed during a conversation?
- A. Sales Summary
- B. Call Summary
- C. Call Explorer
Answer: B
Explanation:
For a sales rep who may miss key details during long sales calls, the Agentforce Specialist should recommend the Call Summary feature. Call Summary uses Einstein Generative AI to automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.
Call Explorer is designed for manually searching through call data but doesn't summarize.
Sales Summary is focused more on summarizing overall sales activity, not call-specific content.
NEW QUESTION # 132
A Salesforce Agentforce Specialist is reviewing the feedback from a customer about the ineffectiveness of the prompt template. What should the Agentforce Specialist do to ensure the prompt template's effectiveness?
- A. Use the Prompt Builder Scorecard to help monitor.
- B. Periodically change the templates grounding object.
- C. Monitor and refine the template based on user feedback.
Answer: A
Explanation:
To address the ineffectiveness of a prompt template reported by a customer, the Salesforce Agentforce Specialist should use the Prompt Builder Scorecard (Option B). This tool is explicitly designed to evaluate and monitor prompt templates against key criteria such as relevance, accuracy, safety, and grounding. By leveraging the scorecard, the specialist can systematically identify weaknesses in the template and make data-driven refinements. While monitoring and refining based on user feedback (Option A) is a general best practice, the Prompt Builder Scorecard is Salesforce's recommended tool for structured evaluation, aligning with documented processes for maintaining prompt effectiveness. Changing the grounding object (Option C) without proper evaluation is reactive and does not address the root cause.
NEW QUESTION # 133
Which use case is best supported by Salesforce Agent's capabilities?
- A. Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.
- B. Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.
- C. Enable data scientists to train predictive AI models with historical CRM data using built-in machine learning capabilities
Answer: B
Explanation:
Salesforce Agent is designed to provide a conversational AI interface that can be utilized by different types of Salesforce users, such as developers, sales agents, and retailers. It acts as an AI-powered assistant that facilitates natural interactions with the system, enabling users to perform tasks and access data easily. This includes tasks like pulling reports, updating records, and generating personalized responses in real time.
NEW QUESTION # 134
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information. What is a possible explanation for the poor prompt performance?
- A. The Einstein Trust Layer is incorrectly configured.
- B. The prompt template version is incompatible with the chosen LLM.
- C. The data being used for grounding is incorrect or incomplete.
Answer: C
Explanation:
UC's custom prompt for summarizing case records is underperforming, and we need to identify a likely cause.
NEW QUESTION # 135
A business stakeholder wants to use Al to generate a summary based on Data Cloud data. Which method(s) should the stakeholder use to access Data Cloud data from Prompt Builder?
- A. Using only external APIs to import Data Cloud data into Prompt Builder
- B. Using Data Cloud related lists and fetching Data Cloud data using prompt-initiated flows
- C. Accessing data model objects (DMQs) directly in Flex templates, using Data Cloud related lists, and fetching Data Cloud data using prompt-initiated flows
Answer: B
Explanation:
The Prompt Builder and Data Cloud Integration Guide explains that Data Cloud information can be accessed directly through Data Cloud related lists or prompt-initiated flows, which fetch relevant data dynamically. The documentation states: "Prompt Builder supports retrieving Data Cloud data using related lists for contextual grounding or invoking flows that query Data Cloud objects at runtime. This enables AI prompts to generate summaries, recommendations, or insights directly from unified customer profiles."
NEW QUESTION # 136
Universal Containers is using Agentforce for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the Agent to match opportunities.
- A. Matched opportunities are limited to the same account.
- B. Matched opportunities were created in the last 12 months.
- C. Matched opportunities have a status of Closed Won from the last 12 months.
Answer: C
NEW QUESTION # 137
Coral Cloud Resorts is uploading thousands of new HTML knowledge articles files for a resort launch. To ensure Agentforce retrieves accurate responses quickly, which chunking strategy should be used when creating a new index?
- A. Section-aware chunking
- B. Semantic-based passage extraction
- C. Conversation-based chunking
Answer: A
Explanation:
In AgentForce documentation on Knowledge Indexing and Chunking Strategies, Salesforce emphasizes that when uploading large volumes of structured content such as HTML or documentation files, the system should use section-aware chunking. The guide states: "Section- aware chunking preserves the logical boundaries of headings, paragraphs, and sub-sections in structured documents like HTML or PDF files, allowing the agent to retrieve contextually accurate and relevant responses quickly." This method ensures that the agent does not split content mid-section or lose contextual relationships between headings and body text. It enhances both retrieval speed and answer precision.
NEW QUESTION # 138
Universal Containers (UC) wants to limit an agent's access to Knowledge articles while deploying the "Answer Questions with Knowledge" action. How should UC achieve this?
- A. Update the Data Library Retriever to filter on a custom field on the Knowledge article.
- B. Define scope instructions to the agent specifying a list of allowed article titles or IDs.
- C. Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.
Answer: C
Explanation:
UC wants to restrict the "Answer Questions with Knowledge" action to a subset of Knowledge articles.
NEW QUESTION # 139
What is a key benefit of the Agent-to-Agent (A2A) protocol?
- A. Provides a standardized framework for cross-vendor agent discovery and communication
- B. Provides a standardized runtime engine for internal agent discovery and communication
- C. Allows auto-onboard third-party agents without additional contracts, trust scores, or shared identity controls
Answer: A
Explanation:
The Agent-to-Agent (A2A) Protocol Overview describes A2A as a standardized framework for cross- vendor agent discovery and communication. The documentation specifies:
"A2A enables secure, interoperable communication between AI agents across vendors, platforms, and ecosystems, using standardized APIs and schemas for message exchange and capability discovery." This allows AgentForce agents to interact with external AI systems or partner agents while maintaining data governance and identity controls.
NEW QUESTION # 140
A Service Agent at Universal Containers (UC) is designed to help customers resolve issues by searching against knowledge articles. Knowledge articles have PDF attachments that add critical details. UC reports that the agent provides excellent summaries of the knowledge articles, but seems completely unaware of the PDF attachments. How should an Agentforce Specialist configure the Data Cloud search index to include the content of these attached files?
- A. Increase article chunk size and token limits for Knowledge indexing so larger contexts capture attachment references.
- B. Enable 'Include Related Attachments' for Knowledge-kav and map the ContentDocumentLink unstructured data model object (UDMO).
- C. Use Data Cloud's `Include Attachments' option and select the ContentDocumentVersion unstructured data model object (UDMO).
Answer: C
Explanation:
The AgentForce Data Cloud Indexing Guide clearly states that to include content from attached files such as PDFs in Knowledge articles, the correct configuration is to enable "Include Attachments" and map the ContentDocumentVersion unstructured data model object (UDMO).
The documentation specifies: "When indexing Knowledge or Case data, enabling the `Include Attachments' option allows the Data Cloud index to extract and embed content from linked ContentDocumentVersion records, ensuring the agent retrieves relevant information from attachments."
NEW QUESTION # 141
An Agentforce wants to ground a new prompt template with the User related list. What should the Agentforce Specialist consider?
- A. The User related list is not supported in prompt templates.
- B. The User related list needs to be included on the record page.
- C. The User related list should have View All access.
Answer: A
Explanation:
Salesforce has restrictions on which objects and related lists can be used for grounding prompt templates. This is likely due to security and privacy concerns related to user data.
While it might seem intuitive to use the User related list to provide context to the LLM, Salesforce prevents this to ensure that sensitive user information is not inadvertently exposed or misused.
Therefore, the Agentforce Specialist needs to explore alternative ways to incorporate the necessary user information into the prompt template, perhaps by using other related objects or fields that are supported.
NEW QUESTION # 142
What is true of Agentforce Testing Center?
- A. Running tests risks modifying CRM data in a production environment.
- B. Agentforce Testing Center can only be used in a production environment.
- C. Running tests does not consume Einstein Requests.
Answer: C
Explanation:
The Agentforce Testing Center is a tool in Agentforce Studio for validating agent performance.
NEW QUESTION # 143
Universal Containers has grounded a prompt template with a related list. During user acceptance testing (UAT), users are not getting the correct responses. What is causing this issue?
- A. The related list is not on the parent object's page layout.
- B. The related list prompt template option is not enabled.
- C. The related list is Read Only.
Answer: A
Explanation:
UC has grounded a prompt template with a related list, but the responses are incorrect during UAT. Grounding with related lists in Agentforce allows the AI to access data from child records linked to a parent object.
NEW QUESTION # 144
Universal Containers is using Agentforce for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the Agent to match opportunities. What is one criterion that Agentforce for Sales uses to match similar opportunities?
- A. Matched opportunities are limited to the same account.
- B. Matched opportunities were created in the last 12 months.
- C. Matched opportunities have a status of Closed Won from the last 12 months.
Answer: C
Explanation:
UC uses Agentforce for Sales to identify similar opportunities, aiding deal closure.
NEW QUESTION # 145
An Agentforce is creating a custom action in Agent. Which option is available for the Agentforce Specialist to choose for the custom copilot action?
- A. Flows
- B. Apex trigger
- C. SOQL
Answer: A
Explanation:
When creating a custom action in Agent, 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 triggers and SOQL are important Salesforce tools, Flows are the recommended method for creating custom actions within Agent because they are declarative and highly adaptable.
NEW QUESTION # 146
Universal Containers (UC) recently attended a major trade show and received thousands of new leads from event badge scans. UC is struggling to follow up with each lead in a timely, personalized way. Leadership wants to:
- Qualify and nurture leads 24/7.
- Provide accurate answers to prospect questions.
- Automatically book meetings with qualified prospects.
- Free up reps to focus on building relationships and closing deals.
Which Agentforce capability should UC implement to meet these goals?
- A. Commerce Agent
- B. SDR Agent
- C. Sales Coach
Answer: B
Explanation:
Universal Containers (UC) needs a solution that can automatically qualify and nurture thousands of new leads 24/7, provide accurate and consistent responses to prospects, schedule meetings for qualified leads, and allow sales representatives to focus on relationship building and closing deals. These needs align precisely with the Agentforce SDR Agent.
According to official AgentForce documentation, "Agentforce SDR helps sales teams qualify and nurture leads at scale, around the clock. It acts as a digital sales development representative capable of engaging new leads instantly, asking the right qualifying questions, answering inquiries accurately using connected Salesforce data, and automatically scheduling meetings on behalf of the sales team." The documentation further explains that the SDR Agent is designed to "personalize outreach, manage follow-up sequences, and book meetings directly from your website or campaign pages." This automation "frees your human reps to focus on high-value interactions and closing opportunities rather than manual lead qualification." By contrast, the Sales Coach capability focuses on guiding and coaching sales representatives internally rather than interacting with prospects, and the Commerce Agent is designed for e- commerce use cases such as assisting shoppers with product discovery and order management- not lead nurturing.
NEW QUESTION # 147
A data science team has trained an XGBoost classification model for product recommendations on Databricks. The Agentforce Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the Agentforce Specialist set this up?
- A. Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
- B. Create the serving endpoint in Databricks, then configure the model using Model Builder.
- C. Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.
Answer: B
Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
Create the Serving Endpoint in Databricks:
The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
Configure the Model Using Model Builder:
After creating the endpoint, the Agentforce Specialist should configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
NEW QUESTION # 148
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