Practice Free Salesforce AI Specialist Exam Online Questions
Question #11
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and
interactions. UC needs audit and feedback
data to be accessible for reporting purposes.
What is a consideration for this requirement?
- A . Storing this data requires Data Cloud to be provisioned.
- B . Storing this data requires a custom object for data to be configured.
- C . Storing this data requires Salesforce big objects.
Correct Answer: A
A
Explanation:
When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data. The Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real-
time, unified data processing required in such AI-driven interactions. Big Objects are more suited for
archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis
capabilities.
Reference: Salesforce Data Cloud Documentation: https://www.salesforce.com/products/data-cloud/overview/
Salesforce Einstein AI Overview: https://www.salesforce.com/products/einstein/overview/
A
Explanation:
When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data. The Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real-
time, unified data processing required in such AI-driven interactions. Big Objects are more suited for
archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis
capabilities.
Reference: Salesforce Data Cloud Documentation: https://www.salesforce.com/products/data-cloud/overview/
Salesforce Einstein AI Overview: https://www.salesforce.com/products/einstein/overview/
Question #12
In addition to Recipient and Sender, which object should an AI Specialist utilize for inserting merge fields into a Sales email template prompt?
- A . Recipient Opportunities
- B . Recipient Account
- C . User Organization
Correct Answer: B
B
Explanation:
Sales Email Template Use Case:
When creating a Sales email template (especially for outreach or follow-up), you often need to reference relevant details about the Account linked to the recipient.
Standard Merge Fields in Salesforce Email Templates:
Recipient (Contact, Lead, or Person receiving the email)
Sender (User sending the email)
Recipient Account (the Account related to that Contact, providing company-level details and other relevant data)
Why Recipient Account?
For Sales communications, referencing the Account data (e.g., Account name, industry, or other custom fields) in an email is very common.
This is especially important for B2B scenarios where the Contact is tied to an Account.
“Recipient Opportunities” could be multiple, so it’s less direct for standard email merges. The “User Organization” is more generic internal information, not typically inserted for personalization to the recipient.
Reference and Study Resources:
Salesforce Help & Training → Email Templates: Merge Fields
Salesforce Trailhead → “Create and Customize Email Templates in Sales Cloud”
Salesforce AI Specialist Study Resources (covers recommended best practices for leveraging standard objects like Account in AI-powered or prompt-based communications)
B
Explanation:
Sales Email Template Use Case:
When creating a Sales email template (especially for outreach or follow-up), you often need to reference relevant details about the Account linked to the recipient.
Standard Merge Fields in Salesforce Email Templates:
Recipient (Contact, Lead, or Person receiving the email)
Sender (User sending the email)
Recipient Account (the Account related to that Contact, providing company-level details and other relevant data)
Why Recipient Account?
For Sales communications, referencing the Account data (e.g., Account name, industry, or other custom fields) in an email is very common.
This is especially important for B2B scenarios where the Contact is tied to an Account.
“Recipient Opportunities” could be multiple, so it’s less direct for standard email merges. The “User Organization” is more generic internal information, not typically inserted for personalization to the recipient.
Reference and Study Resources:
Salesforce Help & Training → Email Templates: Merge Fields
Salesforce Trailhead → “Create and Customize Email Templates in Sales Cloud”
Salesforce AI Specialist Study Resources (covers recommended best practices for leveraging standard objects like Account in AI-powered or prompt-based communications)
Question #13
Universal Containers (UC) recently rolled out Einstein Generative 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 data being used for grounding Is incorrect or incomplete.
- B . The prompt template version is incompatible with the chosen LLM.
- C . The Einstein Trust Layer is incorrectly configured.
Correct Answer: A
A
Explanation:
Poor prompt performance when generating case summaries is often due to the data used for grounding being incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.
Option B (prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality.
Option C (Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.
For more information, refer to Salesforce documentation on grounding AI models and data quality best practices.
A
Explanation:
Poor prompt performance when generating case summaries is often due to the data used for grounding being incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.
Option B (prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality.
Option C (Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.
For more information, refer to Salesforce documentation on grounding AI models and data quality best practices.
Question #14
In Model Playground, which hyperparameters of an existing Salesforce-enabled foundational model can an AI Specialist change?
- A . Temperature, Frequency Penalty, Presence Penalty
- B . Temperature, Top-k sampling, Presence Penalty
- C . Temperature, Frequency Penalty, Output Tokens
Correct Answer: A
A
Explanation:
In Model Playground, an AI specialist working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model: Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model’s responses more focused and deterministic. Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model’s responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer to Salesforce AI Model Playground guidance from Salesforce’s official documentation on foundational model adjustments.
A
Explanation:
In Model Playground, an AI specialist working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model: Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model’s responses more focused and deterministic. Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model’s responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer to Salesforce AI Model Playground guidance from Salesforce’s official documentation on foundational model adjustments.
Question #15
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.
Correct Answer: C
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.
Reference: Salesforce AI Specialist Documentation – Einstein Copilot Supported Actions:
Lists the actions that Einstein Copilot can perform, noting the absence of delete operations.
Salesforce Help – Limitations of Einstein Copilot:
Highlights current limitations, including unsupported actions like deleting records.
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.
Reference: Salesforce AI Specialist Documentation – Einstein Copilot Supported Actions:
Lists the actions that Einstein Copilot can perform, noting the absence of delete operations.
Salesforce Help – Limitations of Einstein Copilot:
Highlights current limitations, including unsupported actions like deleting records.
Question #16
When configuring a prompt template, an AI Specialist previews the results of the prompt template they’ve written. They see two distinct text outputs: Resolution and Response.
Which information does the Resolution text provide?
- A . It shows the full text that is sent to the Trust Layer.
- B . It shows the response from the LLM based on the sample record.
- C . It shows which sensitive data is masked before it is sent to the LLM.
Correct Answer: B
B
Explanation:
When previewing a prompt template in Salesforce, the Resolution text provides the response from the LLM (Large Language Model) based on the data from a sample record. This output shows what the AI model generated in response to the prompt, giving the AI Specialist a chance to review and adjust the response before finalizing the template.
Option B is correct because Resolution displays the actual response generated by the LLM.
Option A refers to sending the text to the Trust Layer, but that’s not what Resolution represents.
Option C relates to data masking, which is shown elsewhere, not under Resolution.
Reference: Salesforce Prompt Builder Overview:
https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm
B
Explanation:
When previewing a prompt template in Salesforce, the Resolution text provides the response from the LLM (Large Language Model) based on the data from a sample record. This output shows what the AI model generated in response to the prompt, giving the AI Specialist a chance to review and adjust the response before finalizing the template.
Option B is correct because Resolution displays the actual response generated by the LLM.
Option A refers to sending the text to the Trust Layer, but that’s not what Resolution represents.
Option C relates to data masking, which is shown elsewhere, not under Resolution.
Reference: Salesforce Prompt Builder Overview:
https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm
Question #17
Universal Containers (UC) is Implementing Service AI Grounding to enhance its customer service operations. UC wants to ensure that its AI- generated responses are grounded in the most relevant data sources. The team needs to configure the system to include all supported objects for grounding.
Which objects should UC select to configure Service AI Grounding?
- A . Case, Knowledge, and Case Notes
- B . Case and Knowledge
- C . Case, Case Emails, and Knowledge
Correct Answer: B
B
Explanation:
Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service operations. They aim to ensure that AI-generated responses are grounded in the most relevant data sources and need to configure the system to include all supported objects for grounding.
Supported Objects for Service AI Grounding:
Case
Knowledge
Case Object:
Role in Grounding: Provides contextual data about customer inquiries, including case details, status, and history.
Benefit: Grounding AI responses in case data ensures that the information provided is relevant to the
specific customer issue being addressed.
Knowledge Object:
Role in Grounding: Contains articles and documentation that offer solutions and information related to common issues.
Benefit: Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on
verified information.
Exclusion of Other Objects:
Case Notes and Case Emails:
Not Supported for Grounding: While useful for internal reference, these objects are not included in the supported objects for Service AI Grounding.
Reason: They may contain sensitive or unstructured data that is not suitable for AI grounding purposes.
Why Options A and C are Incorrect:
Option A (Case, Knowledge, and Case Notes):
Case Notes Not Supported: Case Notes are not among the supported objects for grounding in Service AI.
Option C (Case, Case Emails, and Knowledge):
Case Emails Not Supported: Case Emails are also not included in the list of supported objects for
grounding.
Reference: Salesforce AI Specialist Documentation – Service AI Grounding Configuration: Details the objects supported for grounding AI responses in Service Cloud.
Salesforce Help – Implementing Service AI Grounding: Provides guidance on setting up grounding with Case and Knowledge objects.
Salesforce Trailhead – Enhance Service with AI Grounding: Offers an interactive learning path on using AI grounding in service scenarios.
B
Explanation:
Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service operations. They aim to ensure that AI-generated responses are grounded in the most relevant data sources and need to configure the system to include all supported objects for grounding.
Supported Objects for Service AI Grounding:
Case
Knowledge
Case Object:
Role in Grounding: Provides contextual data about customer inquiries, including case details, status, and history.
Benefit: Grounding AI responses in case data ensures that the information provided is relevant to the
specific customer issue being addressed.
Knowledge Object:
Role in Grounding: Contains articles and documentation that offer solutions and information related to common issues.
Benefit: Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on
verified information.
Exclusion of Other Objects:
Case Notes and Case Emails:
Not Supported for Grounding: While useful for internal reference, these objects are not included in the supported objects for Service AI Grounding.
Reason: They may contain sensitive or unstructured data that is not suitable for AI grounding purposes.
Why Options A and C are Incorrect:
Option A (Case, Knowledge, and Case Notes):
Case Notes Not Supported: Case Notes are not among the supported objects for grounding in Service AI.
Option C (Case, Case Emails, and Knowledge):
Case Emails Not Supported: Case Emails are also not included in the list of supported objects for
grounding.
Reference: Salesforce AI Specialist Documentation – Service AI Grounding Configuration: Details the objects supported for grounding AI responses in Service Cloud.
Salesforce Help – Implementing Service AI Grounding: Provides guidance on setting up grounding with Case and Knowledge objects.
Salesforce Trailhead – Enhance Service with AI Grounding: Offers an interactive learning path on using AI grounding in service scenarios.
Question #18
An AI Specialist is tasked to optimize a business process flow by assigning actions to agents within the Salesforce Agentforce Platform.
What is the correct method for the AI Specialist to assign actions to an Agent?
- A . Assign the action to a Topic First in Agent Builder.
- B . Assign the action to a Topic first on the Agent Actions detail page.
- C . Assign the action to a Topic first on Action Builder.
Correct Answer: C
C
Explanation:
Action Builder is the central place in Salesforce Agentforce where you define and manage actions that your AI agents can perform. This includes connecting actions to various tools and systems. Topics in Agentforce represent the different tasks or intents that an AI agent can handle. By assigning an action to a Topic in Action Builder, you’re essentially telling the agent, "When you encounter this type of request or situation, perform this action."
C
Explanation:
Action Builder is the central place in Salesforce Agentforce where you define and manage actions that your AI agents can perform. This includes connecting actions to various tools and systems. Topics in Agentforce represent the different tasks or intents that an AI agent can handle. By assigning an action to a Topic in Action Builder, you’re essentially telling the agent, "When you encounter this type of request or situation, perform this action."
Question #19
Universal Container’s internal auditing team asks an AI Specialist to verify that address information is properly masked in the prompt being generated.
How should the AI Specialist verify the privacy of the masked data in the Einstein Trust Layer?
- A . Enable data encryption on the address field
- B . Review the platform event logs
- C . Inspect the AI audit trail
Correct Answer: C
C
Explanation:
The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the AI Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is
processed and masked in AI activities.
Reference: "How Salesforce Ensures Trust in AI with Einstein Trust Layer | Salesforce" .
C
Explanation:
The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the AI Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is
processed and masked in AI activities.
Reference: "How Salesforce Ensures Trust in AI with Einstein Trust Layer | Salesforce" .
Question #19
Universal Container’s internal auditing team asks an AI Specialist to verify that address information is properly masked in the prompt being generated.
How should the AI Specialist verify the privacy of the masked data in the Einstein Trust Layer?
- A . Enable data encryption on the address field
- B . Review the platform event logs
- C . Inspect the AI audit trail
Correct Answer: C
C
Explanation:
The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the AI Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is
processed and masked in AI activities.
Reference: "How Salesforce Ensures Trust in AI with Einstein Trust Layer | Salesforce" .
C
Explanation:
The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the AI Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is
processed and masked in AI activities.
Reference: "How Salesforce Ensures Trust in AI with Einstein Trust Layer | Salesforce" .