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Salesforce-AI-Specialist Practice Questions

Question # 1
An AI Specialist wants to include data from the response ofexternal 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.


B. Use External Service Record merge fields.

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:
External Service Record Merge Fields:
Implementation Steps:
Register the External Service:
Create a Named Credential:
Use External Service in Flow:
Configure the Prompt Template:
Why Other Options are Less Suitable:
Option A (Convert the JSON to an XML merge field):
Option C (Use “Add Prompt Instructions” flow element):
References:
Salesforce AI Specialist Documentation -Integrating External Services with Prompt Templates:
Salesforce Help -Using Merge Fields with External Data:
Salesforce Trailhead -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 # 2
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.


B. Add the fine-tuned LLM in Einstein Studio Model Builder.

Explanation: Since security and data privacy are critical, the best option for the AI Specialist is to integrate the fine-tunedLLM (Large Language Model)into Salesforce by adding it toEinstein Studio Model Builder.Einstein Studioallows 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 Studioprovides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found inSalesforce's Einstein Studio documentationon integrating external models.

Question # 3
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


A. Data Cloud-triggered flow

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 # 4
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 associatethe prompt, select a record tovalidate 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.


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.

Explanation: When usingPrompt Builderin a Salesforce org, the correct process involves several important steps:
Select the appropriate prompt template typebased on the use case.
Develop the promptwithin theprompt workspace, where the template is created and customized.
Select CRM-derived grounding datato be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.
Pick the model to usefor generating responses, either using Salesforce's built-in models or custom ones.
Test and validatethe generated responses to ensure accuracy and effectiveness. Option Bis correct as it follows the proper steps for usingPrompt Builder.
Option AandOption Cdo not capture the full process correctly.

Question # 5
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing auto launched flow to query the information from Oracle ERP, which is the system of record for the order fulfillment process.
How should an AI Specialist apply the power of conversational AI to this use case?
A. Create a Flex prompt template in Prompt Builder.
B. Create a custom copilot action which calls a flow.
C. Configure the Integration Flow Standard Action in Einstein Copilot.


B. Create a custom copilot action which calls a flow.

Explanation: To enable Universal Containers service agents to query the current fulfillment status of an order using natural language and leverage an existing auto-launched flow that queries Oracle ERP, the best solution is to create a custom copilot action that calls the flow. This action will allow Einstein Copilot to interact with the flow and retrieve the required order fulfillment information seamlessly. Custom copilot actions can be tailored to call various backend systems or flows in response to user requests.
Option Bis correct because it enables integration betweenEinstein Copilotand the flow that connects to Oracle ERP.
Option A(Flex prompt template) is more suited for static responses and not for invoking flows.
Option C(Integration Flow Standard Action) is not directly related to creating a specific copilot action for this use case.

Question # 6
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


A. Service AI Grounding

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 # 7
An AI Specialist needs to create a Sales Email with a custom prompt template. They need to ground on the following data. Opportunity Products Events near the customer Tone and voice examples. How should the AI Specialist obtain related items?
A. Call prompt initiated flow to fetch and ground the required data.
B. Create a flex template that takes the records in question as inputs.
C. Utilize a standard email template and manually insert the required data fields.


A. Call prompt initiated flow to fetch and ground the required data.

Explanation:

To ground a sales email on Opportunity Products, Events near the customer, and Tone and voice examples, the AI Specialist should use a prompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.

Option B (flex template) does not provide the ability to fetch dynamic data from Salesforce records automatically.

Option C (manual insertion) would not allow for the dynamic and automated grounding of data required for custom prompts.

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.


A. The number of tokens generated by the dynamic nature of the prompt template will vary by record.

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.
References:
Salesforce Developer Documentation onToken Limits for Generative AI Models Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)

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