Practice Free Salesforce AI Specialist Exam Online Questions
Question #1
Which use case is best supported by Salesforce Einstein Copilot’s capabilities?
- A . Bring together a conversational interface for interacting with AI for all Salesforce users, such as developers and ecommerce retailers.
- B . Enable Salesforce admin users to create and train custom large language models (LLMs) using CRM data.
- C . Enable data scientists to train predictive AI models with historical CRM data using built-in machine learning capabilities
Correct Answer: A
A
Explanation:
Salesforce Einstein Copilot 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.
Option A is correct because Einstein Copilot brings a conversational interface that caters to a wide range of users.
Option B and Option C are more focused on developing and training AI models, which are not the
primary functions of Einstein Copilot.
Reference: Salesforce Einstein Copilot Overview:
https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm
A
Explanation:
Salesforce Einstein Copilot 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.
Option A is correct because Einstein Copilot brings a conversational interface that caters to a wide range of users.
Option B and Option C are more focused on developing and training AI models, which are not the
primary functions of Einstein Copilot.
Reference: Salesforce Einstein Copilot Overview:
https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm
Question #2
A data scientist needs to view and manage models in Einstein Studio. The data scientist also needs to create prompt templates in Prompt Builder.
Which permission sets should an AI Specialist assign to the data scientist?
- A . Data Cloud Admin and Prompt Template Manager
- B . Prompt Template Manager and Prompt Template User
- C . Prompt Template User and Data Cloud Admin
Correct Answer: A
A
Explanation:
To allow a data scientist to view and manage models in Einstein Studio and create prompt templates in Prompt Builder, the AI Specialist should assign the Data Cloud Admin and Prompt Template Manager permission sets.
Data Cloud Admin provides access to manage and oversee models within Einstein Studio.
Prompt Template Manager gives the user the ability to create and manage prompt templates within Prompt Builder.
Option A is correct because it assigns the necessary permissions for both managing models and creating prompt templates.
Option B and Option C are incorrect as they do not provide the correct combination of permissions
for managing models and building prompts.
Reference: Salesforce Permissions Documentation:
https://help.salesforce.com/s/articleView?id=sf.perm_sets_overview.htm
A
Explanation:
To allow a data scientist to view and manage models in Einstein Studio and create prompt templates in Prompt Builder, the AI Specialist should assign the Data Cloud Admin and Prompt Template Manager permission sets.
Data Cloud Admin provides access to manage and oversee models within Einstein Studio.
Prompt Template Manager gives the user the ability to create and manage prompt templates within Prompt Builder.
Option A is correct because it assigns the necessary permissions for both managing models and creating prompt templates.
Option B and Option C are incorrect as they do not provide the correct combination of permissions
for managing models and building prompts.
Reference: Salesforce Permissions Documentation:
https://help.salesforce.com/s/articleView?id=sf.perm_sets_overview.htm
Question #3
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
Correct Answer: A
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
Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.
Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.
Why Not Flex or Sales Email Prompt Templates?
Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.
Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting
outreach or follow-up messages). This template is not specifically built to populate a field on a
record.
Conclusion
For automatically populating the Description field with AI-generated content, the Field Generation prompt template (Option A) is the correct choice.
Salesforce AI Specialist Reference & Documents
Salesforce Documentation: Prompt Template Types
Explains various template types (Field Generation, Flex, Email, etc.) and their typical use cases. Salesforce AI Specialist Study Guide
Highlights Field Generation prompt templates for populating or updating record fields with AI-generated text.
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
Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.
Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.
Why Not Flex or Sales Email Prompt Templates?
Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.
Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting
outreach or follow-up messages). This template is not specifically built to populate a field on a
record.
Conclusion
For automatically populating the Description field with AI-generated content, the Field Generation prompt template (Option A) is the correct choice.
Salesforce AI Specialist Reference & Documents
Salesforce Documentation: Prompt Template Types
Explains various template types (Field Generation, Flex, Email, etc.) and their typical use cases. Salesforce AI Specialist Study Guide
Highlights Field Generation prompt templates for populating or updating record fields with AI-generated text.
Question #4
Universal Containers’ service team wants to customize the standard case summary response from Einstein Copilot.
What should the AI Specialist do to achieve this?
- A . Customize the standard Record Summary template for the Case object,
- B . Summarize the Case with a standard copilot action.
- C . Create a custom Record Summary prompt template for the Case object.
Correct Answer: C
C
Explanation:
To customize the case summary response from Einstein Copilot, the AI Specialist should create a custom Record Summary prompt template for the Case object. This allows Universal Containers to tailor the way case data is summarized, ensuring the output aligns with specific business requirements or user preferences.
Option A (customizing the standard Record Summary template) does not provide the flexibility required for deep customization.
Option B (standard Copilot action) won’t allow customization; it will only use default settings. Refer to Salesforce Prompt Builder documentation for guidance on creating custom templates for record summaries.
C
Explanation:
To customize the case summary response from Einstein Copilot, the AI Specialist should create a custom Record Summary prompt template for the Case object. This allows Universal Containers to tailor the way case data is summarized, ensuring the output aligns with specific business requirements or user preferences.
Option A (customizing the standard Record Summary template) does not provide the flexibility required for deep customization.
Option B (standard Copilot action) won’t allow customization; it will only use default settings. Refer to Salesforce Prompt Builder documentation for guidance on creating custom templates for record summaries.
Question #4
Universal Containers’ service team wants to customize the standard case summary response from Einstein Copilot.
What should the AI Specialist do to achieve this?
- A . Customize the standard Record Summary template for the Case object,
- B . Summarize the Case with a standard copilot action.
- C . Create a custom Record Summary prompt template for the Case object.
Correct Answer: C
C
Explanation:
To customize the case summary response from Einstein Copilot, the AI Specialist should create a custom Record Summary prompt template for the Case object. This allows Universal Containers to tailor the way case data is summarized, ensuring the output aligns with specific business requirements or user preferences.
Option A (customizing the standard Record Summary template) does not provide the flexibility required for deep customization.
Option B (standard Copilot action) won’t allow customization; it will only use default settings. Refer to Salesforce Prompt Builder documentation for guidance on creating custom templates for record summaries.
C
Explanation:
To customize the case summary response from Einstein Copilot, the AI Specialist should create a custom Record Summary prompt template for the Case object. This allows Universal Containers to tailor the way case data is summarized, ensuring the output aligns with specific business requirements or user preferences.
Option A (customizing the standard Record Summary template) does not provide the flexibility required for deep customization.
Option B (standard Copilot action) won’t allow customization; it will only use default settings. Refer to Salesforce Prompt Builder documentation for guidance on creating custom templates for record summaries.
Question #6
After a successful implementation of Agentforce Sates Agent with sales users. Universal Containers now aims to deploy it to the service team.
Which key consideration should the AI Specialist keep in mind for this deployment?
- A . Assign the Agentforce for Service permission to the Service Cloud users.
- B . Assign the standard service actions to Agentforce Service Agent.
- C . Review and test standard and custom Agent topics and actions for Service Center use cases.
Correct Answer: C
C
Explanation:
When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:
Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.
Option A: Permissions like "Agentforce for Service" are necessary but secondary to functional compatibility.
Option B: Standard service actions must be mapped to Agentforce, but testing ensures they function
as intended.
Reference: Salesforce Help: Einstein Agent Setup
Emphasizes reviewing "topics and actions for different user groups (Sales vs. Service)."
C
Explanation:
When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:
Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.
Option A: Permissions like "Agentforce for Service" are necessary but secondary to functional compatibility.
Option B: Standard service actions must be mapped to Agentforce, but testing ensures they function
as intended.
Reference: Salesforce Help: Einstein Agent Setup
Emphasizes reviewing "topics and actions for different user groups (Sales vs. Service)."
Question #7
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
- A . Find similar requests and provide actions that need to be executed
- B . Identify the best matching actions and correct order of execution
- C . Determine a user’s access and sort actions by priority to be executed
Correct Answer: B
B
Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user’s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and
governance than by the LLM.
Reference: Salesforce Einstein Documentation on Einstein Copilot Actions Salesforce AI Documentation on Large Language Models
B
Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user’s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and
governance than by the LLM.
Reference: Salesforce Einstein Documentation on Einstein Copilot Actions Salesforce AI Documentation on Large Language Models
Question #8
Universal Containers (UC) wants to assess Salesforce’s generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein’s generative AI service.
No data is used for LLM training or product improvements by third- party LLMs.
No data is retained outside of UC’s Salesforce org.
The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should the AI Specialist highlight to UC that addresses these requirements?
- A . Prompt Defense
- B . Zero-Data Retention Policy
- C . Data Masking
Correct Answer: B
B
Explanation:
Universal Containers (UC) has concerns about data privacy when using Salesforce’s generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data. The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:
No data is used for training or product improvements by third-party LLMs.
No data is retained outside of the customer’s Salesforce organization.
The LLM provider cannot access any customer data.
This policy aligns perfectly with UC’s requirements for keeping their data safe while leveraging generative AI capabilities.
Prompt Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.
Reference: Salesforce Einstein Trust Layer Documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm
B
Explanation:
Universal Containers (UC) has concerns about data privacy when using Salesforce’s generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data. The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:
No data is used for training or product improvements by third-party LLMs.
No data is retained outside of the customer’s Salesforce organization.
The LLM provider cannot access any customer data.
This policy aligns perfectly with UC’s requirements for keeping their data safe while leveraging generative AI capabilities.
Prompt Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.
Reference: Salesforce Einstein Trust Layer Documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm
Question #9
An Al Specialist is creating a custom action for Agentforce.
Which setting should the AI Specialist test and iterate on to ensure the action performs as expected?
- A . Action Input
- B . Action Name
- C . Action Instructions
Correct Answer: C
C
Explanation:
To ensure a custom action in Agentforce performs as expected, the AI Specialist must focus on Action Instructions.
Here’s why:
Action Instructions define the logic, parameters, and steps the AI should follow to execute the action.
They include:
How input data is processed.
API calls or Apex invocations.
Conditional logic (e.g., decision trees).
Testing and iterating on these instructions ensures alignment with the intended workflow. For example, incorrect API endpoint references or misconfigured parameters in the instructions will cause failures.
Action Input (Option A) refers to the data provided to the action. While validating input formats is important, inputs are static once defined. The primary issue lies in whether the instructions correctly use the inputs.
Action Name (Option B) is a descriptive label and does not affect functionality.
Salesforce Documentation Support:
Salesforce Einstein Bots & Custom Actions Guide highlights that Action Instructions are where the "core logic" resides, requiring rigorous testing (Source: Einstein Bots Developer Guide).
Trailhead Module "Build Custom Actions for Einstein Bots" emphasizes refining instructions to handle edge cases and validate outputs (Source: Trailhead).
By iterating on Action Instructions, the AI Specialist ensures the action’s logic, integrations, and error handling are robust.
C
Explanation:
To ensure a custom action in Agentforce performs as expected, the AI Specialist must focus on Action Instructions.
Here’s why:
Action Instructions define the logic, parameters, and steps the AI should follow to execute the action.
They include:
How input data is processed.
API calls or Apex invocations.
Conditional logic (e.g., decision trees).
Testing and iterating on these instructions ensures alignment with the intended workflow. For example, incorrect API endpoint references or misconfigured parameters in the instructions will cause failures.
Action Input (Option A) refers to the data provided to the action. While validating input formats is important, inputs are static once defined. The primary issue lies in whether the instructions correctly use the inputs.
Action Name (Option B) is a descriptive label and does not affect functionality.
Salesforce Documentation Support:
Salesforce Einstein Bots & Custom Actions Guide highlights that Action Instructions are where the "core logic" resides, requiring rigorous testing (Source: Einstein Bots Developer Guide).
Trailhead Module "Build Custom Actions for Einstein Bots" emphasizes refining instructions to handle edge cases and validate outputs (Source: Trailhead).
By iterating on Action Instructions, the AI Specialist ensures the action’s logic, integrations, and error handling are robust.
Question #10
An AI Specialist 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.
Correct Answer: A
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, the AI Specialist must 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 to race 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).
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, the AI Specialist must 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 to race 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).