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Salesforce-AI-Specialist Practice Questions

Question # 1
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.


A. Surround them with triple quotes (""").

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 # 2
A sales rep at Universal Containers is extremely busy and sometimes will have very long sales calls on voice and video calls and might miss key details. They are just starting to adopt new generative AI features.
Which Einstein Generative AI feature should an AI Specialist recommend to help the rep get the details they might have missed during a conversation?
A. Call Summary
B. Call Explorer
C. Sales Summary


A. Call Summary

Explanation: For a sales rep who may miss key details during long sales calls, the AI Specialist should recommend the Call Summary feature. Call Summary uses Einstein Generative AI to automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.
Call Explorer is designed for manually searching through call data but doesn't summarize.
Sales Summary is focused more on summarizing overall sales activity, not call specific content.
For more details, refer to Salesforce's Call Summary documentation on how AI generated summaries can improve sales rep productivity.

Question # 3
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


B. Field Generation

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-inthe-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.

Question # 4
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 # 5
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)

Question # 6
Amid their busy schedules, sales reps at Universal Containers dedicate time to follow up with prospects and existing clients via email regarding renewals or new deals. They spend many hours throughout the week reviewing past communications and details about their customers before performing their outreach. Which standard Copilot action helps sales reps draft personalized emails to prospects by generating text based on previous successful communications?
A. Einstein Copilot Action: Find Similar Opportunities
B. Einstein Copilot Action: Draft or Revise Sales Email
C. Einstein Copilot Action: Summarize Record


B. Einstein Copilot Action: Draft or Revise Sales Email

Explanation:

For sales reps who need to draft personalized emails based on previous communications, the AI Specialist should recommend the Einstein Copilot Action: Draft or Revise Sales Email. This action uses AI to generate or revise email content, leveraging past successful communications to create personalized and relevant outreach to prospects or clients.

Find Similar Opportunities is used for opportunity matching, not email drafting.

Summarize Record provides a summary of customer data but does not directly help with drafting emails.

Question # 7
Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions.
Which use case functionality of Model Builder aligns with UC's request?
A. Product recommendation prediction
B. Customer churn prediction
C. Contract Renewal Date prediction


B. Customer churn prediction

Explanation: Customer churn prediction is the best use case for Model Builder in addressing Universal Containers' concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn, UC can proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.
Option Bis correct because customer churn prediction aligns with UC's need to reduce cancellations through proactive measures.
Option A(product recommendation prediction) is unrelated to contract cancellations.
Option C(contract renewal date prediction) addresses timing but does not focus on predicting potential cancellations.

Question # 8
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.


A. Utilize the audit trail that captures and stores all LLM submitted prompts in Data Cloud.

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? Al though 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.

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