Practice Free Salesforce AI Specialist Exam Online Questions
Question #31
Universal Containers’ data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?
- A . Model Builder
- B . App Builder
- C . Copilot Builder
Correct Answer: A
A
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform’s native AI capabilities. Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation. Salesforce AI Specialist
Reference: For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
A
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform’s native AI capabilities. Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation. Salesforce AI Specialist
Reference: For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
Question #32
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data
using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?
- A . Predict customer sentiment toward a promotion message.
- B . Predict customer lifetime value of an account.
- C . Predict most popular products from new product catalog.
Correct Answer: B
B
Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
Option B is the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
Option A (customer sentiment) is typically handled through NLP models, while Option C (product popularity) is more of a marketing analysis use case.
Reference: Salesforce Einstein Studio Use Case Overview:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview
B
Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
Option B is the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
Option A (customer sentiment) is typically handled through NLP models, while Option C (product popularity) is more of a marketing analysis use case.
Reference: Salesforce Einstein Studio Use Case Overview:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview
Question #33
What does it mean when a prompt template version is described as immutable?
- A . Only the latest version of a template can be activated.
- B . Every modification on a template will be saved as a new version automatically.
- C . Prompt template version is activated; no further changes can be saved to that version.
Correct Answer: C
C
Explanation:
When a prompt template version is immutable, it means that once the version is activated, it cannot be edited or modified. This ensures consistency in production environments where changes could disrupt workflows.
Option A is incorrect: Any version (not just the latest) can be activated, depending on the use case. Option D is incorrect: Modifications require manually creating a new version; automatic versioning is not enforced.
Option C is correct: Activation locks the version, enforcing immutability.
Reference: Salesforce Help: Prompt Template Versioning
States that "activated prompt template versions are immutable and cannot be edited."
C
Explanation:
When a prompt template version is immutable, it means that once the version is activated, it cannot be edited or modified. This ensures consistency in production environments where changes could disrupt workflows.
Option A is incorrect: Any version (not just the latest) can be activated, depending on the use case. Option D is incorrect: Modifications require manually creating a new version; automatic versioning is not enforced.
Option C is correct: Activation locks the version, enforcing immutability.
Reference: Salesforce Help: Prompt Template Versioning
States that "activated prompt template versions are immutable and cannot be edited."
Question #34
Universal Containers implements three custom actions to get three distinct types of sales summaries for its users. Users are complaining that they are not getting the right summary based on their utterances.
What should the AI Specialist investigate as the root cause?
- A . Review that the custom action Is assigned to an Agent.
- B . Review the action Instructions to ensure they are unique.
- C . Ensure the input and output types are correctly chosen.
Correct Answer: B
B
Explanation:
The root cause of users receiving incorrect sales summaries lies in non-unique action instructions (Option B). In Einstein Bots, custom actions are triggered based on how well user utterances align with the action instructions defined for each action. If the instructions for the three custom actions overlap or lack specificity, the bot’s natural language processing (NLP) cannot reliably distinguish between them, leading to mismatched responses.
Steps to Investigate:
Review Action Instructions: Ensure each custom action has distinct, context-specific instructions. For example:
Action 1: "Summarize quarterly sales by region."
Action 2: "Generate a product-wise sales breakdown for the current fiscal year."
Action 3: "Provide a comparison of sales performance between online and in-store channels." Ambiguous or overlapping instructions (e.g., "Get sales summary") cause confusion.
Test Utterance Matching: Use Einstein Bot’s training tools to validate if user utterances map to the correct action. Overlap indicates instruction ambiguity.
Refine Instructions: Incorporate keywords or phrases unique to each sales summary type to improve intent detection.
Why Other Options Are Incorrect:
B
Explanation:
The root cause of users receiving incorrect sales summaries lies in non-unique action instructions (Option B). In Einstein Bots, custom actions are triggered based on how well user utterances align with the action instructions defined for each action. If the instructions for the three custom actions overlap or lack specificity, the bot’s natural language processing (NLP) cannot reliably distinguish between them, leading to mismatched responses.
Steps to Investigate:
Review Action Instructions: Ensure each custom action has distinct, context-specific instructions. For example:
Action 1: "Summarize quarterly sales by region."
Action 2: "Generate a product-wise sales breakdown for the current fiscal year."
Action 3: "Provide a comparison of sales performance between online and in-store channels." Ambiguous or overlapping instructions (e.g., "Get sales summary") cause confusion.
Test Utterance Matching: Use Einstein Bot’s training tools to validate if user utterances map to the correct action. Overlap indicates instruction ambiguity.
Refine Instructions: Incorporate keywords or phrases unique to each sales summary type to improve intent detection.
Why Other Options Are Incorrect:
Question #35
A Salesforce Administrator wants to generate personalized, targeted emails that incorporate customer interaction data. The admin wants to leverage large language models (LLMs) to write the emails, and wants to reuse templates for different products and customers.
Which solution approach should the admin leverage?
- A . Use sales Email standard templates
- B . Create a t field Generation prompt template type
- C . Create a Sales Email prompt template type.
Correct Answer: C
C
Explanation:
To generate personalized emails using LLMs while reusing templates:
Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.
Option A: Standard email templates lack LLM integration and dynamic personalization.
Option B: "t field Generation" is not a valid Salesforce prompt template type.
Reference: Salesforce Help: Sales Email Prompt Templates
Describes using Sales Email prompt templates to "generate targeted emails using dynamic data and LLMs."
C
Explanation:
To generate personalized emails using LLMs while reusing templates:
Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.
Option A: Standard email templates lack LLM integration and dynamic personalization.
Option B: "t field Generation" is not a valid Salesforce prompt template type.
Reference: Salesforce Help: Sales Email Prompt Templates
Describes using Sales Email prompt templates to "generate targeted emails using dynamic data and LLMs."
Question #36
Universal Containers implements Custom Copilot Actions to enhance its customer service operations. The development team needs to understand the core components of a Custom Copilot Action to ensure proper configuration and functionality.
What should the development team review in the Custom Copilot Action configuration to identify one of the core components of a Custom Copilot Action?
- A . Instructions
- B . Output Types
- C . Action Triggers
Correct Answer: A
A
Explanation:
Instructions: This is a core component of Custom Copilot Actions. Instructions tell the AI model what the action should do and how it should be executed. Clear and concise instructions are crucial for the action to function correctly and provide the expected outcome.
Let’s look at why the other options are not the primary core component:
Output Types: While important for defining the kind of data the action produces, it’s not the core defining element of the action itself.
Action Triggers: These determine when the action is initiated, but they don’t define the core functionality of the action.
A
Explanation:
Instructions: This is a core component of Custom Copilot Actions. Instructions tell the AI model what the action should do and how it should be executed. Clear and concise instructions are crucial for the action to function correctly and provide the expected outcome.
Let’s look at why the other options are not the primary core component:
Output Types: While important for defining the kind of data the action produces, it’s not the core defining element of the action itself.
Action Triggers: These determine when the action is initiated, but they don’t define the core functionality of the action.
Question #36
Universal Containers implements Custom Copilot Actions to enhance its customer service operations. The development team needs to understand the core components of a Custom Copilot Action to ensure proper configuration and functionality.
What should the development team review in the Custom Copilot Action configuration to identify one of the core components of a Custom Copilot Action?
- A . Instructions
- B . Output Types
- C . Action Triggers
Correct Answer: A
A
Explanation:
Instructions: This is a core component of Custom Copilot Actions. Instructions tell the AI model what the action should do and how it should be executed. Clear and concise instructions are crucial for the action to function correctly and provide the expected outcome.
Let’s look at why the other options are not the primary core component:
Output Types: While important for defining the kind of data the action produces, it’s not the core defining element of the action itself.
Action Triggers: These determine when the action is initiated, but they don’t define the core functionality of the action.
A
Explanation:
Instructions: This is a core component of Custom Copilot Actions. Instructions tell the AI model what the action should do and how it should be executed. Clear and concise instructions are crucial for the action to function correctly and provide the expected outcome.
Let’s look at why the other options are not the primary core component:
Output Types: While important for defining the kind of data the action produces, it’s not the core defining element of the action itself.
Action Triggers: These determine when the action is initiated, but they don’t define the core functionality of the action.
Question #38
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should an AI Specialist recommend?
- A . Use Apex to connect to an external LLM and ground the prompt.
- B . Use BYO-LLM functionality in Einstein Studio,
- C . Use Flow and External Services to bring data from an external LLM.
Correct Answer: B
B
Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem. Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.
Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
Option C focuses on Flow and External Services, which is more about data integration and isn’t ideal
for working with LLMs.
Reference: Salesforce Einstein Studio BYO-LLM Documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_llm.htm
B
Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem. Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.
Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
Option C focuses on Flow and External Services, which is more about data integration and isn’t ideal
for working with LLMs.
Reference: Salesforce Einstein Studio BYO-LLM Documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_llm.htm
Question #39
Universal Containers plans to enhance the customer support team’s productivity using AI.
Which specific use case necessitates the use of Prompt Builder?
- A . Creating a draft of a support bulletin post for new product patches
- B . Creating an Al-generated customer support agent performance score
- C . Estimating support ticket volume based on historical data and seasonal trends
Correct Answer: A
A
Explanation:
The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post for new product patches. Prompt Builder allows the AI Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.
Option B (agent performance score) would likely involve predictive modeling, not prompt generation.
Option C (estimating support ticket volume) would require data analysis and predictive tools, not prompt building.
For more details, refer to Salesforce’s Prompt Builder documentation for generative AI content creation.
A
Explanation:
The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post for new product patches. Prompt Builder allows the AI Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.
Option B (agent performance score) would likely involve predictive modeling, not prompt generation.
Option C (estimating support ticket volume) would require data analysis and predictive tools, not prompt building.
For more details, refer to Salesforce’s Prompt Builder documentation for generative AI content creation.
Question #40
An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client.
How should the AI Specialist integrate the custom LLM into Salesforce?
- A . Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
- B . Add the fine-tuned LLM in Einstein Studio Model Builder.
- C . Enable model endpoint on OpenAl and make callouts to the model to generate emails.
Correct Answer: B
B
Explanation:
Since security and data privacy are critical, the best option for the AI Specialist is to integrate the fine-tuned LLM (Large Language Model) into Salesforce by adding it to Einstein Studio Model Builder. Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce’s environment, adhering to data privacy standards.
Option A (embedding via iFrame) is less secure and doesn’t integrate deeply with Salesforce’s data and security models.
Option C (making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.
Einstein Studio provides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found in Salesforce’s Einstein Studio documentation on integrating external models.
B
Explanation:
Since security and data privacy are critical, the best option for the AI Specialist is to integrate the fine-tuned LLM (Large Language Model) into Salesforce by adding it to Einstein Studio Model Builder. Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce’s environment, adhering to data privacy standards.
Option A (embedding via iFrame) is less secure and doesn’t integrate deeply with Salesforce’s data and security models.
Option C (making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.
Einstein Studio provides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found in Salesforce’s Einstein Studio documentation on integrating external models.