Salesforce Salesforce AI Specialist Salesforce Certified AI Specialist Exam Online Training

exams

4 months ago

Question #1

Leadership needs to populate a dynamic form field with a summary or description created by a large language model (LLM) to facilitate more productive conversations with customers. Leadership also wants to keep a human in the loop to be considered in their AI strategy.

Which prompt template type should the AI Specialist recommend?

A . Sales Email
B . Field Generation
C . Record Summary

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Correct Answer: B
B

Explanation:

The correct answer is Field Generation because this template type is designed to dynamically populate form fields with content generated by a large language model (LLM). In this scenario, leadership wants a dynamic form field that contains a summary or description generated by AI to aid customer interactions. Additionally, they want to keep a human in the loop, meaning the generated content will likely be reviewed or edited by a person before it’s finalized, which aligns with the Field Generation prompt template.

Field Generation: This prompt type allows you to generate content for specific fields in Salesforce, leveraging large language models to create dynamic and contextual information. It ensures that AI content is available within the record where needed, but it allows human oversight or review, supporting the "human-in-the-loop" strategy.

Sales Email: This prompt type is mainly used for generating email content for outreach or responses, which doesn’t align directly with populating fields in a form.

Record Summary: While this option might seem close, it is typically used to summarize entire records for high-level insights rather than filling specific fields with dynamic content based on AI generation. Salesforce AI Specialist

Reference: You can explore more about these prompt templates and AI capabilities through Salesforce documentation and official resources on Prompt Builder: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_templates_overview.htm

Question #2

Universal Containers is considering leveraging the Einstein Trust Layer in conjunction with Einstein Generative AI Audit Data.

Which audit data is available using the Einstein Trust Layer?

A . Response accuracy and offensiveness score
B . Hallucination score and bias score
C . Masked data and toxicity score

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Correct Answer: C
C

Explanation:

Universal Containers is considering the use of the Einstein Trust Layer along with Einstein Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to use AI by offering features like data masking and toxicity assessment.

The audit data available through the Einstein Trust Layer includes information about masked data― which ensures sensitive information is not exposed―and the toxicity score, which evaluates the generated content for inappropriate or harmful language.

Reference: Salesforce AI Specialist Documentation – Einstein Trust Layer: Details the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.

Question #3

Universal Containers wants to make a sales proposal and directly use data from multiple unrelated objects (standard and custom) in a prompt template.

What should the AI Specialist recommend?

A . Create a Flex template to add resources with standard and custom objects as inputs.
B . Create a prompt template passing in a special custom object that connects the records temporarily,
C . Create a prompt template-triggered flow to access the data from standard and custom objects.

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Correct Answer: A
A

Explanation:

Universal Containers needs to generate a sales proposal using data from multiple unrelated standard and custom objects within a prompt template. The most effective way to achieve this is by using a Flex template.

Flex templates in Salesforce allow AI specialists to create prompt templates that can accept inputs

from multiple sources, including various standard and custom objects. This flexibility enables the

direct use of data from unrelated objects without the need to create intermediary custom objects or

complex flows.

Reference: Salesforce AI Specialist Documentation – Flex Templates: Explains how Flex templates can be utilized to incorporate data from multiple sources, providing a flexible solution for complex data

requirements in prompt templates.

Question #4

What is an AI Specialist able to do when the “Enrich event logs with conversation data" setting in Einstein Copilot is enabled?

A . View the user click path that led to each copilot action.
B . View session data including user Input and copilot responses for sessions over the past 7 days.
C . Generate details reports on all Copilot conversations over any time period.

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Correct Answer: B
B

Explanation:

When the "Enrich event logs with conversation data" setting is enabled in Einstein Copilot, it allows an AI Specialist or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.

This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping AI specialists track AI behavior and user engagement.

Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.

Option C, generating detailed reports over any time period, is incorrect because this specific feature

is limited to data for the past 7 days.

Salesforce AI Specialist

Reference: You can refer to this documentation for further insights:

https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm

Question #5

Universal Containers’ current AI data masking rules do not align with organizational privacy and security policies and requirements.

What should an AI Specialist recommend to resolve the issue?

A . Enable data masking for sandbox refreshes.
B . Configure data masking in the Einstein Trust Layer setup.
C . Add new data masking rules in LLM setup.

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Correct Answer: B
B

Explanation:

When Universal Containers’ AI data masking rules do not meet organizational privacy and security standards, the AI Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.

Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.

Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.

The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model

and ensures compliance with privacy regulations.

Salesforce AI Specialist

Reference: For more information, refer to:

https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_data_masking.htm

Question #6

An administrator wants to check the response of the Flex prompt template they’ve built, but the preview button is greyed out.

What is the reason for this?

A . The records related to the prompt have not been selected.
B . The prompt has not been saved and activated,
C . A merge field has not been inserted in the prompt.

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Correct Answer: A
A

Explanation:

When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can’t be previewed since there is no content to generate based on the template.

Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality.

Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.

Ensuring that the related records are correctly linked is crucial for testing and previewing how the

prompt will function in real use cases.

Salesforce AI Specialist

Reference: Refer to the documentation on troubleshooting Flex templates here:

https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm

Question #7

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

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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

Question #8

An AI Specialist built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors.

What is the cause of the random nature of this error?

A . The number of tokens generated by the dynamic nature of the prompt template will vary by record.
B . The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
C . The number of tokens that can be processed by the LLM varies with total user demand.

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Correct Answer: A
A

Explanation:

The reason behind the token limit errors lies in the dynamic nature of the prompt template used in Field Generation. In Salesforce’s AI generative models, each prompt and its corresponding output are subject to a token limit, which encompasses both the input and output of the large language model (LLM). Since the prompt template dynamically adjusts based on the specific data of each record, the number of tokens varies per record. Some records may generate longer outputs based on their data attributes, pushing the token count beyond the allowable limit for the LLM, resulting in token limit errors.

This behavior explains why users experience random failures―it is dependent on the specific data used in each case. For certain records, the combined input and output may fall within the token limit, while for others, it may exceed it. This variation is intrinsic to how dynamic templates interact with large language models.

Salesforce provides guidance in their documentation, stating that prompt template design should

take into account token limits and suggests testing with varied records to avoid such random errors.

It does not mention switching to Flex template type as a solution, nor does it suggest that token

limits fluctuate with user demand. Token limits are a constant defined by the model itself,

independent of external user load.

Reference: Salesforce Developer Documentation on Token Limits for Generative AI Models

Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)

Question #9

An administrator is responsible for ensuring the security and reliability of Universal Containers’ (UC) CRM dat

a. UC needs enhanced data protection and up-to-date AI capabilities. UC also needs to include relevant

information from a Salesforce record to be merged with the prompt.

Which feature in the Einstein Trust Layer best supports UC’s need?

A . Data masking
B . Dynamic grounding with secure data retrieval
C . Zero-data retention policy

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Correct Answer: B
B

Explanation:

Dynamic grounding with secure data retrieval is a key feature in Salesforce’s Einstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.

Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model’s outputs are trustworthy and reliable for business use.

The other options are less aligned with the requirement:

Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.

Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.

Reference: Salesforce Developer Documentation on Einstein Trust Layer Salesforce Security Documentation for AI and Data Privacy

Question #10

A Salesforce Administrator is exploring the capabilities of Einstein Copilot to enhance user interaction within their organization. They are particularly interested in how Einstein Copilot processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Einstein Copilot directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users.

How does Einstein Copilot handle user requests In Salesforce?

A . Einstein Copilot will trigger a flow that utilizes a prompt template to generate the message.
B . Einstein Copilot will perform an HTTP callout to an LLM provider.
C . Einstein Copilot analyzes the user’s request and LLM technology is used to generate and display
the appropriate response.

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Correct Answer: C
C

Explanation:

Einstein Copilot is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Einstein Copilot analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.

Option C accurately describes this process. Einstein Copilot does not necessarily trigger a flow

(Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it

integrates LLM capabilities to provide immediate and intelligent responses, facilitating a broad range

of user requests.

Reference: Salesforce AI Specialist Documentation – Einstein Copilot Overview: Details how Einstein Copilot employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem. Salesforce Help – How Einstein Copilot Works: Explains the underlying mechanisms of how Einstein Copilot processes user requests using AI technologies.

Question #11

Universal Containers wants to utilize Einstein for Sales to help sales reps reach their sales quotas by providing Al-generated plans containing guidance and steps for closing deals.

Which feature should the AI Specialist recommend to the sales team?

A . Find Similar Deals
B . Create Account Plan
C . Create Close Plan

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Correct Answer: C
C

Explanation:

The "Create Close Plan" feature is designed to help sales reps by providing AI-generated strategies and steps specifically focused on closing deals. This feature leverages AI to analyze the current state of opportunities and generate a plan that outlines the actions, timelines, and key steps required to move deals toward closure. It aligns directly with the sales team’s need to meet quotas by offering actionable insights and structured plans.

Find Similar Deals (Option A) helps sales reps discover opportunities similar to their current deals but doesn’t offer a plan for closing.

Create Account Plan (Option B) focuses on long-term strategies for managing accounts, which might include customer engagement and retention, but doesn’t focus on deal closure. Salesforce AI Specialist

Reference: For more information on using AI for sales, visit:

https://help.salesforce.com/s/articleView?id=sf.einstein_for_sales_overview.htm

Question #12

How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and meaningful responses?

A . Masked data will be de-masked during response journey.
B . Masked data will be de-masked during request journey.
C . Responses that do not meet the relevance threshold will be automatically rejected.

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Correct Answer: A
A

Explanation:

The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de-masking it during the response journey.

How It Works:

Data Masking in the Request Journey:

Sensitive Data Identification: Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business information, or any other data deemed sensitive.

Masking Sensitive Data: Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.

Processing by the LLM:

Masked Input: The LLM processes the masked prompt and generates a response based on the masked data.

No Exposure of Sensitive Data: Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output. De-masking in the Response Journey:

Re-insertion of Sensitive Data: After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.

Providing Meaningful Responses: This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate. Maintaining Data Security: At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.

Why Option A is Correct:

De-masking During Response Journey: The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.

Balancing Security and Utility: This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.

Why Options B and C are Incorrect:

Option B (Masked data will be de-masked during request journey):

Incorrect Process: De-masking during the request journey would expose sensitive data before it reaches the LLM, defeating the purpose of masking and compromising data security.

Option C (Responses that do not meet the relevance threshold will be automatically rejected): Irrelevant to Data Protection: While the Einstein Trust Layer does enforce relevance thresholds to filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the protection of sensitive data. It addresses response quality rather than data security.

Reference: Salesforce AI Specialist Documentation – Einstein Trust Layer Overview:

Explains how the Trust Layer masks sensitive data in prompts and re-inserts it after LLM processing to protect data privacy.

Salesforce Help – Data Masking and De-masking Process:

Details the masking of sensitive data before sending to the LLM and the de-masking process during the response journey.

Salesforce AI Specialist Exam Guide – Security and Compliance in AI:

Outlines the importance of data protection mechanisms like the Einstein Trust Layer in AI

implementations.

Conclusion:

The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.

Question #13

Universal Containers (UC) wants to enable its sales team to get insights into product and competitor names mentioned during calls.

How should UC meet this requirement?

A . Enable Einstein Conversation Insights, assign permission sets, define recording managers, and customize insights with up to 50 competitor names.
B . Enable Einstein Conversation Insights, connect a recording provider, assign permission sets, and customize insights with up to 25 products.
C . Enable Einstein Conversation Insights, enable sales recording, assign permission sets, and customize insights with up to 50 products.

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Correct Answer: C
C

Explanation:

To provide the sales team with insights into product and competitor names mentioned during calls, Universal Containers should:

Enable Einstein Conversation Insights: Activates the feature that analyzes call recordings for valuable insights.

Enable Sales Recording: Allows calls to be recorded within Salesforce without needing an external recording provider.

Assign Permission Sets: Grants the necessary permissions to sales team members to access and utilize conversation insights.

Customize Insights: Configure the system to track mentions of up to 50 products and 50 competitors, providing tailored insights relevant to the organization’s needs.

Option C accurately reflects these steps. Option A mentions defining recording managers but omits enabling sales recording within Salesforce. Option B suggests connecting a recording provider and limits customization to 25 products, which does not fully meet UC’s requirements.

Reference: Salesforce AI Specialist Documentation – Setting Up Einstein Conversation Insights: Provides

instructions on enabling conversation insights and sales recording.

Salesforce Help – Customizing Conversation Insights: Details how to customize insights with up to 50 products and competitors.

Salesforce AI Specialist Exam Guide: Outlines best practices for implementing AI features like Einstein

Conversation Insights in a sales context.

Question #14

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

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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

Question #15

A service agent is looking at a custom object that stores travel information. They recently received a weather alert and now need to cancel flights for the customers that are related with this itinerary. The service agent needs to review the Knowledge articles about canceling and rebooking the customer flights.

Which Einstein Copilot capability helps the agent accomplish this?

A . Execute tasks based on available actions, answering questions using information from accessible Knowledge articles.
B . Invoke a flow which makes a call to external data to create a Knowledge article.
C . Generate a Knowledge article based off the prompts that the agent enters to create steps to cancel flights.

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Correct Answer: C
C

Explanation:

In this scenario, the Einstein Copilot capability that best helps the agent is its ability to execute tasks based on available actions and answer questions using data from Knowledge articles. Einstein Copilot can assist the service agent by providing relevant Knowledge articles on canceling and rebooking flights, ensuring that the agent has access to the correct steps and procedures directly within the workflow.

This feature leverages the agent’s existing context (the travel itinerary) and provides actionable insights or next steps from the relevant Knowledge articles to help the agent quickly resolve the customer’s needs.

The other options are incorrect:

B refers to invoking a flow to create a Knowledge article, which is unrelated to the task of retrieving existing Knowledge articles.

C focuses on generating Knowledge articles, which is not the immediate need for this situation where the agent requires guidance on existing procedures.

Reference: Salesforce Documentation on Einstein Copilot

Trailhead Module on Einstein for Service

Question #16

An AI Specialist has created a copilot custom action using flow as the reference action type. However, it is not delivering the expected results to the conversation preview, and therefore needs troubleshooting.

What should the AI Specialist do to identify the root cause of the problem?

A . In Copilot Builder within the Dynamic Panel, turn on dynamic debugging to show the inputs and outputs.
B . Copilot Builder within the Dynamic Panel, confirm selected action and observe the values in Input and Output sections.
C . In Copilot Builder, verify the utterance entered by the user and review session event logs for debug information.

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Correct Answer: A
A

Explanation:

When troubleshooting a copilot custom action using flow as the reference action type, enabling dynamic debugging within Copilot Builder’s Dynamic Panel is the most effective way to identify the root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both the inputs and outputs of the flow, which helps identify where the action might be failing or not delivering the expected results.

Option B, confirming selected actions and observing the Input and Output sections, is useful for monitoring flow configuration but does not provide the deep diagnostic details available with

dynamic debugging.

Option C, verifying the user utterance and reviewing session event logs, could provide helpful context, but dynamic debugging is the primary tool for identifying issues with inputs and outputs in real time.

Salesforce AI Specialist

Reference: To explore more about dynamic debugging in Copilot Builder, see:

https://help.salesforce.com/s/articleView?id=sf.copilot_custom_action_debugging.htm

Question #17

A support team handles a high volume of chat interactions and needs a solution to provide quick, relevant responses to customer inquiries.

Responses must be grounded in the organization’s knowledge base to maintain consistency and accuracy.

Which feature in Einstein for Service should the support team use?

A . Einstein Service Replies
B . Einstein Reply Recommendations
C . Einstein Knowledge Recommendations

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Correct Answer: B
B

Explanation:

The support team should use Einstein Reply Recommendations to provide quick, relevant responses to customer inquiries that are grounded in the organization’s knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.

Einstein Service Replies (Option A) is focused on generating replies but doesn’t have the same emphasis on grounding responses in the knowledge base.

Einstein Knowledge Recommendations (Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.

Salesforce AI Specialist

Reference: For more information on Einstein Reply Recommendations:

https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm

Question #18

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.

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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.

Question #19

Where should the AI Specialist go to add/update actions assigned to a copilot?

A . Copilot Actions page, the record page for the copilot action, or the Copilot Action Library tab
B . Copilot Actions page or Global Actions
C . Copilot Detail page, Global Actions, or the record page for the copilot action

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Correct Answer: A
A

Explanation:

To add or update actions assigned to a copilot, an AI Specialist can manage this through several areas:

Copilot Actions Page: This is the central location where copilot actions are managed and configured. Record Page for the Copilot Action: From the record page, individual copilot actions can be updated or modified.

Copilot Action Library Tab: This tab serves as a repository where predefined or custom actions for Copilot can be accessed and modified.

These areas provide flexibility in managing and updating the actions assigned to Copilot, ensuring that the AI assistant remains aligned with business requirements and processes. The other options are incorrect:

B misses the Copilot Action Library, which is crucial for managing actions.

C includes the Copilot Detail page, which isn’t the primary place for action management.

Reference: Salesforce Documentation on Managing Copilot Actions Salesforce AI Specialist Guide on Copilot Action Management

Question #20

Universal Containers wants to reduce overall agent handling time minimizing the time spent typing

routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields.

Which combination of Einstein for Service features enables this effort?

A . Einstein Service Replies and Work Summaries
B . Einstein Reply Recommendations and Case Summaries
C . Einstein Reply Recommendations and Case Classification

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Correct Answer: C
C

Explanation:

Universal Containers aims to reduce overall agent handling time by minimizing the time agents spend typing routine answers for common questions during chats and by reducing post-chat analysis through suggesting values for case fields.

To achieve these objectives, the combination of Einstein Reply Recommendations and Case Classification is the most appropriate solution.

Question #20

A . Einstein Service Replies and Work Summaries
B . Einstein Reply Recommendations and Case Summaries
C . Einstein Reply Recommendations and Case Classification

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Question #20

A . Einstein Service Replies and Work Summaries
B . Einstein Reply Recommendations and Case Summaries
C . Einstein Reply Recommendations and Case Classification

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Question #23

Universal Containers (UC) is looking to enhance its operational efficiency. UC has recently adopted Salesforce and is considering implementing Einstein Copilot to improve its processes.

What is a key reason for implementing Einstein Copilot?

A . Improving data entry and data cleansing
B . Allowing AI to perform tasks without user interaction
C . Streamlining workflows and automating repetitive tasks

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Correct Answer: C
C

Explanation:

The key reason for implementing Einstein Copilot is its ability to streamline workflows and automate repetitive tasks. By leveraging AI, Einstein Copilot can assist users in handling mundane, repetitive processes, such as automatically generating insights, completing actions, and guiding users through complex processes, all of which significantly improve operational efficiency.

Option A (Improving data entry and cleansing) is not the primary purpose of Einstein Copilot, as its focus is on guiding and assisting users through workflows.

Option B (Allowing AI to perform tasks without user interaction) does not accurately describe the role of Einstein Copilot, which operates interactively to assist users in real time. Salesforce AI Specialist

Reference: More details can be found in the Salesforce documentation:

https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_overview.htm

Question #24

Northern Trail Outfitters (NTO) wants to configure Einstein Trust Layer in its production org but is unable to see the option on the Setup page.

After provisioning Data Cloud, which step must an Al Specialist take to make this option available to NTO?

A . Turn on Einstein Copilot.
B . Turn on Einstein Generative AI.
C . Turn on Prompt Builder.

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Correct Answer: B
B

Explanation:

For Northern Trail Outfitters (NTO) to configure the Einstein Trust Layer, the Einstein Generative AI feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.

Option A (Turning on Einstein Copilot) is unrelated to the setup of the Einstein Trust Layer, which focuses more on generative AI interactions and data handling.

Option C (Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it

does not enable the Einstein Trust Layer.

Salesforce AI Specialist

Reference: For more details on the Einstein Trust Layer and setup steps:

https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_overview.htm

Question #25

Universal Containers wants to implement a solution in Salesforce with a custom UX that allows users to enter a sales order number.

Subsequently, the system will invoke a custom prompt template to create and display a summary of the sales order header and sales order details.

Which solution should an AI Specialist implement to meet this requirement?

A . Create a screen flow to collect sales order number and invoke the prompt template using the standard "Prompt Template" flow action.
B . Create a template-triggered prompt flow and invoke the prompt template using the standard “Prompt Template” flow action.
C . Create an autolaunched flow and invoke the prompt template using the standard “Prompt Template" flow action.

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Correct Answer: A
A

Explanation:

To implement a solution where users enter a sales order number and the system generates a summary, the AI Specialist should create a screen flow to collect the sales order number and invoke the prompt template. The standard "Prompt Template" flow action can then be used to trigger the custom prompt, providing a summary of the sales order header and details.

Option B, creating a template-triggered prompt flow, is not necessary for this scenario because the requirement is to directly collect input through a screen flow.

Option C, using an autolaunched flow, would be inappropriate here because the solution requires user interaction (entering a sales order number), which is best suited to a screen flow. Salesforce AI Specialist

Reference: For further guidance on creating prompt templates with flows:

https://help.salesforce.com/s/articleView?id=sf.prompt_template_flow_integration.htm

Question #26

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 user does not have the Prompt Template User permission set assigned.
B . The prompt template associated with summary field is not activated for that user.
C . The user does not have the field Generative AI User permission set assigned.

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Correct Answer: C
C

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.

Why not A? The Prompt Template User permission set relates specifically to users who need access to prompt templates for interacting with Einstein GPT, but it’s not directly related to the visibility of AI-enabled field icons.

Why not B? While a prompt template might need to be activated, this is not the primary issue here. The question states that other users with the same profile can see the icon, so the problem is more likely to be permissions-based for this particular user.

For more detailed information, you can review Salesforce documentation on permission sets related to AI capabilities at Salesforce AI Documentation and Einstein GPT permissioning guidelines.

Question #27

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.

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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/

Question #28

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

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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.

Question #29

How should an organization use the Einstein Trust layer to audit, track, and view masked data?

A . Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.
B . In Setup, use Prompt Builder to send a prompt to the LLM requesting for the masked data.
C . Access the audit trail in Setup and export all user-generated prompts.

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Correct Answer: A
A

Explanation:

The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce’s AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:

Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.

Why not B? Using Prompt Builder in Setup to send prompts to the LLM is for creating and managing prompts, not for auditing or tracking data. It does not interact directly with the audit trail functionality.

Why not C? Although the audit trail can be accessed in Setup, the user-generated prompts are primarily tracked in the Data Cloud for broader control, auditing, and analysis. Setup is not the primary tool for exporting or managing these audit logs.

More information on auditing AI interactions can be found in the Salesforce AI Trust Layer documentation, which outlines how organizations can manage and track generative AI interactions securely.

Question #30

An AI Specialist implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce. The AI Specialist needs to configure the system to use the most accurate and up-to-date information for email generation.

Which grounding technique should the AI Specialist use?

A . Ground with Apex Merge Fields
B . Ground with Record Merge Fields
C . Automatic grounding using Draft with Einstein feature

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Correct Answer: C
C

Explanation:

For Einstein Sales Emails to generate personalized follow-up emails, it is crucial to ground the email content with the most up-to-date and accurate information. Grounding refers to connecting the AI model with real-time data. The most appropriate technique in this case is Ground with Record Merge Fields. This method ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.

Record Merge Fields ensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.

Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.

Automatic grounding using Draft with Einstein is a different feature where Einstein automatically

drafts the email, but it does not specifically ground the content with record-specific data like Record

Merge Fields.

Reference: Salesforce Einstein Sales Emails Documentation:

https://help.salesforce.com/s/articleView?id=release-notes.rn_einstein_sales_emails.htm

Question #31

Universal Containers needs a tool that can analyze voice and video call records to provide insights on competitor mentions, coaching opportunities, and other key information. The goal is to enhance the team’s performance by identifying areas for improvement and competitive intelligence.

Which feature provides insights about competitor mentions and coaching opportunities?

A . Call Summaries
B . Einstein Sales Insights
C . Call Explorer

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Correct Answer: C
C

Explanation:

For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls. Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.

Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based

analysis.

Reference: Salesforce Einstein Conversation Insights Documentation:

https://help.salesforce.com/s/articleView?id=einstein_conversation_insights.htm

Question #32

An AI Specialist at Universal Containers (UC) Is tasked with creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is

captured and monitored for adoption and possible enhancements.

Which prompt template type should the AI Specialist use and which consideration should they review?

A . Flex, and that Dynamic Fields is enabled
B . Field Generation, and that Dynamic Fields is enabled
C . Field Generation, and that Dynamic Forms is enabled

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Correct Answer: B
B

Explanation:

When creating a custom prompt template to populate a field with generated output, the most appropriate template type is Field Generation. This template is specifically designed for generating field-specific outputs using generative AI.

Additionally, the AI Specialist must ensure that Dynamic Fields are enabled. Dynamic Fields allow the system to use real-time data inputs from related records or fields when generating content, ensuring that the AI output is contextually accurate and relevant. This is crucial when populating specific fields with AI-generated content, as it ensures the data source remains dynamic and up-to-date.

The Einstein Trust Layer will track and audit the interactions to ensure the organization can monitor AI adoption and make necessary enhancements based on AI usage patterns.

For further reading, refer to Salesforce’s guidelines on Field Generation templates and the Einstein Trust Layer.

Question #33

Universal Containers plans to implement prompt templates that utilize the standard foundation models.

What should the AI Specialist consider when building prompt templates in Prompt Builder?

A . Include multiple-choice questions within the prompt to test the LLM’s understanding of the context.
B . Ask it to role-play as a character in the prompt template to provide more context to the LLM.
C . Train LLM with data using different writing styles including word choice, intensifiers, emojis, and punctuation.

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Correct Answer: C
C

Explanation:

When building prompt templates in Prompt Builder, it is essential to consider how the Large Language Model (LLM) processes and generates outputs. Training the LLM with various writing styles, such as different word choices, intensifiers, emojis, and punctuation, helps the model better understand diverse writing patterns and produce more contextually appropriate responses.

This approach enhances the flexibility and accuracy of the LLM when generating outputs for different use cases, as it is trained to recognize various writing conventions and styles. The prompt template should focus on providing rich context, and this stylistic variety helps improve the model’s adaptability.

Options A and B are less relevant because adding multiple-choice questions or role-playing scenarios doesn’t contribute significantly to improving the AI’s output generation quality within standard business contexts.

For more details, refer to Salesforce’s Prompt Builder documentation and LLM tuning strategies.

Question #34

Universal Containers (UC) has a mature Salesforce org with a lot of data in cases and Knowledge articles. UC is concerned that there are many legacy fields, with data that might not be applicable for Einstein AI to draft accurate email responses.

Which solution should UC use to ensure Einstein AI can draft responses from a defined data source?

A . Service AI Grounding
B . Work Summaries
C . Service Replies

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Correct Answer: A
A

Explanation:

Service AI Grounding is the solution that Universal Containers should use to ensure Einstein AI drafts responses based on a well-defined data source. Service AI Grounding allows the AI model to be anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email replies) are accurate, relevant, and drawn from up-to-date information, such as Knowledge articles or cases.

Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of responses and avoids inaccuracies caused by outdated or irrelevant fields.

Work Summaries and Service Replies are useful features but do not address the need for grounding AI outputs in specific, current data sources like Service AI Grounding does.

For more details, you can refer to Salesforce’s Service AI Grounding documentation for managing AI-generated content based on accurate data sources.

Question #35

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

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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.

Question #36

What is the main purpose of Prompt Builder?

A . A tool for developers to use in Visual Studio Code that creates prompts for Apex programming, assisting developers in writing code more efficiently.
B . A tool that enables companies to create reusable prompts for large language models (LLMs), bringing generative AI responses to their flow of work
C . A tool within Salesforce offering real-time Al-powered suggestions and guidance to users, Improving productivity and decision-making.

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Correct Answer: B
B

Explanation:

Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales. It is not a tool for Apex programming (as in option A).

It is also not limited to real-time suggestions as mentioned in option

C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.

Reference: Salesforce Prompt Builder Overview:

https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm

Question #37

Universal Containers (UC) wants to offer personalized service experiences and reduce agent handling time with Al-generated email responses, grounded in Knowledge base.

Which AI capability should UC use?

A . Einstein Email Replies
B . Einstein Service Replies for Email
C . Einstein Generative Service Replies for Email

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Correct Answer: B
B

Explanation:

For Universal Containers (UC) to offer personalized service experiences and reduce agent handling time using AI-generated responses grounded in the Knowledge base, the best solution is Einstein Service Replies for Email. This capability leverages AI to automatically generate responses to service-related emails based on historical data and the Knowledge base, ensuring accuracy and relevance while saving time for service agents.

Einstein Email Replies (option A) is more suited for sales use cases.

Einstein Generative Service Replies for Email (option C) could be a future offering, but as of now, Einstein Service Replies for Email is the correct choice for grounded, knowledge-based responses.

Reference: Einstein Service Replies Overview:

https://help.salesforce.com/s/articleView?id=sf.einstein_service_replies.htm

Question #38

Universal Containers (UC) wants to use Flow to bring data from unified Data Cloud objects to prompt templates.

Which type of flow should UC use?

A . Data Cloud-triggered flow
B . Template-triggered prompt flow
C . Unified-object linking flow

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Correct Answer: B
B

Explanation:

In this scenario, Universal Containers wants to bring data from unified Data Cloud objects into prompt templates, and the best way to do that is through a Data Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.

Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.

For more detailed guidance, refer to Salesforce documentation on Data Cloud-triggered flows and Data Cloud integrations with generative AI solutions.

Question #39

Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer’s toxicity scoring to assess the content’s safety level.

What does a safety category score of 1 indicate in the Einstein Generative Toxicity Score?

A . Not safe
B . Safe
C . Moderately safe

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Correct Answer: B
B

Explanation:

In the Einstein Trust Layer, the toxicity scoring system is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1 indicates that the content is deemed safe.

The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by the Einstein Trust Layer.

For further reference, check Salesforce’s official Einstein Trust Layer documentation regarding toxicity scoring for AI-generated content.

Question #40

Universal Containers has an active standard email prompt template that does not fully deliver on the business requirements.

Which steps should an AI Specialist take to use the content of the standard prompt email template in question and customize it to fully meet the business requirements?

A . Save as New Template and edit as needed.
B . Clone the existing template and modify as needed.
C . Save as New Version and edit as needed.

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Correct Answer: A
A

Explanation:

When an active standard email prompt template doesn’t meet the business requirements, the best approach is to clone the existing template and modify it as needed. Cloning allows the AI Specialist to preserve the original template while making adjustments to fit specific business needs. This ensures that any customizations are applied without altering the original standard template. Saving as a new version is typically used for versioning changes in the same template, while Save as New Template creates a brand-new template without linking to the existing one. Cloning provides a balance, allowing modifications while retaining the original structure for future reference.

For more details, refer to Salesforce Prompt Builder documentation for guidance on cloning and modifying templates.

Question #41

The marketing team at Universal Containers is looking for a way personalize emails based on customer behavior, preferences, and purchase history.

Why should the team use Einstein Copilot as the solution?

A . To generate relevant content when engaging with each customer
B . To analyze past campaign performance
C . To send automated emails to all customers

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Correct Answer: A
A

Explanation:

Einstein Copilot is designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team at Universal Containers, this is the perfect solution to create dynamic and relevant email content. By leveraging Einstein Copilot, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.

Option A is correct as Einstein Copilot helps generate real-time, personalized content based on comprehensive data about the customer.

Option B refers more to Einstein Analytics or Marketing Cloud Intelligence, and Option C deals with automation, which isn’t the primary focus of Einstein Copilot.

Reference: Salesforce Einstein Copilot Overview:

https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm

Question #42

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.

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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

Question #43

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.

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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

Question #44

An AI Specialist at Universal Containers is working on a prompt template to generate personalized emails for product demonstration requests from customers. It is important for the Al-generated email to adhere strictly to the guidelines, using only associated opportunity information, and to encourage the recipient to take the desired action.

How should the AI Specialist include these instructions on a new line in the prompt template?

A . Surround them with triple quotes (""").
B . Make sure merged fields are defined.
C . Use curly brackets {} to encapsulate instructions.

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Correct Answer: A
A

Explanation:

In Salesforce prompt templates, instructions that guide how the Large Language Model (LLM) should generate content (in this case, personalized emails) can be included by surrounding the instruction text with triple quotes ("""). This formatting ensures that the LLM adheres to the specific instructions while generating the email content.

The use of triple quotes allows the AI to understand that the enclosed text is a directive for how to approach the task, such as limiting the content to associated opportunity information or encouraging a specific action from the recipient.

Refer to Salesforce Prompt Builder documentation for detailed instructions on how to structure prompts for generative AI.

Question #45

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

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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.

Question #46

Based on the user utterance, “Show me all the customers in New York", which standard Einstein Copilot action will the planner service use?

A . Query Records
B . Select Records
C . Fetch Records

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Correct Answer: A
A

Explanation:

The standard Einstein Copilot action that would be used in response to the user utterance, “Show me all the customers in New York,” is Query Records. This action is responsible for retrieving a set of records from Salesforce based on a specified condition ― in this case, filtering customers by location (New York).

Query Records is the action that fetches relevant data based on the criteria provided in the user’s input.

Select Records is more about picking specific records from an already presented list.

Fetch Records is not a standard term used in this context for the action.

Refer to Einstein Copilot documentation on how Copilot actions work with natural language queries and data retrieval.

Question #47

An AI Specialist wants to ground a new prompt template with the User related list.

What should the AI Specialist consider?

A . The User related list should have View All access.
B . The User related list needs to be included on the record page.
C . The User related list is not supported in prompt templates.

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Correct Answer: C
C

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 AI 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.

Question #48

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

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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

Question #49

An AI Specialist wants to use the related lists from an account in a custom prompt template.

What should the AI Specialist consider when configuring the prompt template?

A . The text encoding (for example, UTF-8, ASCII) option
B . The maximum number of related list merge fields
C . The choice between XML and JSON rendering formats for the list

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Correct Answer: B
B

Explanation:

When configuring a custom prompt template to use related lists, the AI Specialist must be aware of the maximum number of related list merge fields that can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it’s important to monitor and optimize the number of merge fields used.

Option B is correct because there is a limit on how many related list merge fields can be included in a prompt template.

Option A (text encoding) and Option C (XML/JSON rendering) are not key considerations in this context.

Reference: Salesforce Prompt Builder Documentation:

https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm

Question #50

Universal Containers is using Einstein Copilot for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the copilot to match opportunities.

What is one criteria that Einstein Copilot 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 last 12 months.

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Correct Answer: C
C

Explanation:

When Einstein Copilot for Sales matches similar opportunities, one of the primary criteria used is whether the opportunities have a status of Closed Won within the last 12 months. This is a key factor in identifying successful patterns that could help close current deals. By focusing on opportunities that have been recently successful, Einstein Copilot can provide relevant insights and suggestions to sales reps to help them close similar deals faster.

For more information, review Salesforce Einstein Copilot documentation related to opportunity matching and sales success patterns.

Question #51

Universal Containers (UC) wants to enable its sales reps to explore opportunities that are similar to previously won opportunities by entering the utterance, "Show me other opportunities like this one."

How should UC achieve this in Einstein Copilot?

A . Use the standard Copilot action.
B . Create a custom Copilot action calling a flow.
C . Create a custom Copilot action calling an Apex class.

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Correct Answer: A
A

Explanation:

Universal Containers can achieve the request to explore similar opportunities by using the standard Copilot action. Einstein Copilot has built-in actions to handle natural language queries, such as “Show me other opportunities like this one.” The standard action will process the query and return results based on predefined matching criteria like opportunity details and past Closed Won deals. This approach avoids the need to create custom flows or Apex classes, leveraging out-of-the-box functionality.

For further details, refer to Einstein Copilot for Sales documentation regarding standard actions and natural language processing.

Question #52

Universal Containers is planning a marketing email about products that most closely match a customer’s expressed interests.

What should an AI Specialist recommend to generate this email?

A . Standard email marketing template using Apex or flows for matching interest in products
B . Custom sales email template which is grounded with interest and product information
C . Standard email draft with Einstein and choose standard email template

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Correct Answer: B
B

Explanation:

To generate an email about products that closely match a customer’s expressed interests, an AI Specialist should recommend using a custom sales email template that is grounded with interest and product information. This ensures that the email content is personalized based on the customer’s preferences, increasing the relevance of the marketing message.

Using grounding ensures that the generative AI pulls the correct data related to customer interests and product matches, making the email more effective.

For more information, refer to Salesforce documentation on grounding AI-generated content and email personalization strategies.

Question #53

An AI Specialist is creating a custom action in Einstein Copilot.

Which option is available for the AI Specialist to choose for the custom copilot action?

A . Apex trigger
B . SOQL
C . Flows

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Correct Answer: C
C

Explanation:

When creating a custom action in Einstein Copilot, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the AI 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 Einstein Copilot because they are declarative and highly adaptable.

For further guidance, refer to Salesforce Flow documentation and Einstein Copilot customization resources.

Question #54

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

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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

Question #55

What is the correct process to leverage Prompt Builder in a Salesforce org?

A . Select the appropriate prompt template type to use, select one of Salesforce’s standard prompts, determine the object to associate the prompt, select a record to validate against, and associate the prompt to an action.
B . Select the appropriate prompt template type to use, develop the prompt within the prompt workspace, select resources to dynamically insert CRM-derived grounding data, pick the model to use, and test and validate the generated responses.
C . Enable the target object for generative prompting, develop the prompt within the prompt workspace, select records to fine-tune and ground the response, enable the Trust Layer, and associate the prompt to an action.

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Correct Answer: B
B

Explanation:

When using Prompt Builder in a Salesforce org, the correct process involves several important steps:

Select the appropriate prompt template type based on the use case.

Develop the prompt within the prompt workspace, where the template is created and customized. Select CRM-derived grounding data to be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.

Pick the model to use for generating responses, either using Salesforce’s built-in models or custom ones.

Test and validate the generated responses to ensure accuracy and effectiveness.

Option B is correct as it follows the proper steps for using Prompt Builder.

Option A and Option C do not capture the full process correctly.

Reference: Salesforce Prompt Builder Documentation:

https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm

Question #56

An AI Specialist wants to include data from the response of external service invocation (REST API callout) into the prompt template.

How should the AI Specialist meet this requirement?

A . Convert the JSON to an XML merge field.
B . Use External Service Record merge fields.
C . Use “Add Prompt Instructions” flow element.

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Correct Answer: B
B

Explanation:

An AI Specialist wants to include data from the response of an external service invocation (REST API callout) into a prompt template. The goal is to incorporate dynamic data retrieved from an external API into the AI-generated content.

Solution:

Use External Service Record Merge Fields

External Service Integration:

Definition: External Services in Salesforce allow the integration of external REST APIs into Salesforce without custom code.

Registration: The external service must be registered in Salesforce, defining the API’s schema and methods.

External Service Record Merge Fields:

Purpose: Enables the inclusion of data from external service responses directly into prompt templates using merge fields.

Functionality:

Dynamic Data Inclusion: Allows prompt templates to access and use data returned from REST API callouts.

Merge Fields Syntax: Use merge fields in the prompt template to reference specific data points from the API response.

Implementation Steps:

Register the External Service:

Use External Services to register the REST API in Salesforce. Define the API’s schema, including methods and data structures. Create a Named Credential:

Configure authentication and endpoint details for the external API.

Use External Service in Flow:

Build a Flow that invokes the external service and captures the response. Ensure the flow outputs the necessary data for use in the prompt template.

Configure the Prompt Template:

Use External Service Record merge fields in the prompt template to reference data from the flow’s output.

Syntax Example: {{flowOutputVariable.fieldName}}

Why Other Options are Less Suitable:

Option A (Convert the JSON to an XML merge field):

Irrelevance: Converting JSON to XML merge fields is unnecessary and complicates the process. Unsupported Method: Salesforce prompt templates do not support direct inclusion of XML merge fields from JSON conversion.

Option C (Use “Add Prompt Instructions” flow element):

Purpose of Add Prompt Instructions:

Allows adding instructions to the prompt within a flow but does not facilitate including external data.

Limitation: Does not directly help in incorporating external service responses into the prompt

template.

Reference: Salesforce AI Specialist Documentation – Integrating External Services with Prompt Templates:

Explains how to use External Services and merge fields in prompt templates.

Salesforce Help – Using Merge Fields with External Data:

Provides guidance on referencing external data in templates using merge fields.

Salesforce Trailhead – External Services and Flow:

Offers a practical understanding of integrating external APIs using External Services and Flow.

Conclusion:

By using External Service Record merge fields, the AI Specialist can effectively include data from external REST API responses into prompt templates, ensuring that the AI-generated content is enriched with up-to-date and relevant external data.

Question #57

Universal Containers (UC) has a legacy system that needs to integrate with Salesforce. UC wishes to create a digest of account action plans using the generative API feature.

Which API service should UC use to meet this requirement?

A . REST API
B . Metadata API
C . SOAP API

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Correct Answer: A
A

Explanation:

To create a digest of account action plans using the generative API feature, Universal Containers should use the REST API. The REST API is ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.

Metadata API is used for retrieving and deploying metadata, not for data operations like generating summaries.

SOAP API is an older API used for integration but is less flexible compared to REST for this specific use case.

For more details, refer to Salesforce REST API documentation regarding using REST for data integration and generating content.

Question #58

The sales team at a hotel resort would like to generate a guest summary about the guests’ interests and provide recommendations based on their activity preferences captured in each guest profile. They want the summary to be available only on the contact record page.

Which AI capability should the team use?

A . Einstein Copilot
B . Prompt Builder
C . Model Builder

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Correct Answer: B
B

Explanation:

The sales team at a hotel resort wants to generate a guest summary about guests’ interests and provide recommendations based on their activity preferences captured in each guest profile. They require the summary to be available only on the contact record page.

Solution:

Use Prompt Builder to create a prompt template that generates the desired summary and displays it on the contact record page.

Prompt Builder:

Purpose: Allows the creation of custom prompt templates that leverage AI to generate content

based on Salesforce data.

Functionality:

Field Generation Templates: Can be used to populate fields on records with AI-generated summaries. Customization: Enables the AI Specialist to design prompts that utilize data from the guest profiles to produce personalized summaries and recommendations.

Relevance to the Use Case:

The sales team wants the summary to be available on the contact record page, which aligns with the capabilities of Prompt Builder to generate and display content on specific record pages. Implementation Steps:

Create a Field Generation Prompt Template:

Use Prompt Builder to create a new prompt template of type Field Generation.

Design the prompt to instruct the AI to generate a summary based on the guest’s interests and

activity preferences.

Include Relevant Data:

Use merge fields to include data from the guest profile in the prompt.

Ensure that the prompt accesses the necessary fields to generate accurate recommendations.

Configure the Contact Page Layout:

Add the field that will display the AI-generated summary to the contact record page layout. Ensure that the field is only visible where appropriate, adhering to the requirement of availability only on the contact record page.

Why Not Einstein Copilot or Model Builder:

Option A (Einstein Copilot):

Purpose: Einstein Copilot is a conversational AI assistant designed to interact with users through natural language.

Mismatch with Requirements:

The team wants a static summary displayed on the contact record page, not an interactive conversational experience.

Option C (Model Builder):

Purpose: Model Builder is used to create custom AI models for predictions and classifications.

Inapplicability:

Building a custom model is unnecessary for generating text summaries based on existing data.

Model Builder does not directly provide functionality to generate and display summaries on record pages.

Reference: Salesforce AI Specialist Documentation – Prompt Builder Overview: Provides an introduction to Prompt Builder and its capabilities. Salesforce Help – Creating Field Generation Prompt Templates:

Guides on creating prompt templates that generate content for fields on records.

Salesforce Trailhead – Customize AI Content with Prompt Builder:

Offers hands-on experience in building and customizing prompt templates.

Conclusion:

By utilizing Prompt Builder, the sales team can create a customized prompt template that generates personalized guest summaries and recommendations based on activity preferences. This solution meets the requirement of displaying the summary only on the contact record page, enhancing the team’s ability to engage with guests effectively.

Question #59

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.

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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.

Question #60

What should an AI Specialist consider when using related list merge fields in a prompt template associated with an Account object in Prompt Builder?

A . The Activities related list on the Account object is not supported because it is a polymorphic field.
B . If person accounts have been enabled, merge fields will not be available for the Account object.
C . Prompt generation will yield no response when there is no related list associated with an Account in runtime.

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Correct Answer: A
A

Explanation:

When using related list merge fields in a prompt template associated with the Account object in Prompt Builder, the Activities related list is not supported due to it being a polymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.

Option B is incorrect because person accounts do not limit the availability of merge fields for the Account object.

Option C is irrelevant since even if no related lists are available at runtime, the prompt can still generate based on other available data fields.

For more information, refer to Salesforce documentation on supported fields and limitations in Prompt Builder.

Question #61

Universal Containers (UC) wants to use the Draft with Einstein feature in Sales Cloud to create a personalized introduction email.

After creating a proposed draft email, which predefined adjustment should UC choose to revise the draft with a more casual tone?

A . Make Less Formal
B . Enhance Friendliness
C . Optimize for Clarity

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Correct Answer: A
A

Explanation:

When Universal Containers uses the Draft with Einstein feature in Sales Cloud to create a personalized email, the predefined adjustment to Make Less Formal is the correct option to revise the draft with a more casual tone. This option adjusts the wording of the draft to sound less formal, making the communication more approachable while still maintaining professionalism. Enhance Friendliness would make the tone more positive, but not necessarily more casual.

Optimize for Clarity focuses on making the draft clearer but doesn’t adjust the tone.

For more details, see Salesforce documentation on Einstein-generated email drafts and tone adjustments.

Question #62

Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Einstein Copilot.

How should the AI Specialist monitor Copilot’s usability and the assignment of actions?

A . Run a report on the Platform Debug Logs.
B . Query the Copilot log data using the metadata API.
C . Run Einstein Copilot Analytics.

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Correct Answer: C
C

Explanation:

To monitor Einstein Copilot’s usability and the assignment of actions, the AI Specialist should run Einstein Copilot Analytics. This feature provides insights into how often Copilot is used, the types of actions it is handling, and overall user engagement with the system. It’s the most effective way to track Copilot’s performance and usage patterns.

Platform Debug Logs are not relevant for tracking user behavior or the assignment of Copilot actions. Querying the Copilot log data via the Metadata API would not provide the necessary insights in a structured manner.

For more details, refer to Salesforce’s Copilot Analytics documentation for tracking AI-driven interactions.