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Salesforce-AI-Specialist Practice Questions

Question # 1
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 # 2
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?
A. Secure Data Retrieval and Grounding
B. Data Masking
C. Prompt Defense


C. Prompt Defense

Explanation: Prompt Defenseis a feature in theEinstein Trust Layerthat helps minimize the risks ofjailbreakingandprompt injection attacks. These attacks occur when malicious users try to manipulate the AI model by providing unintended inputs.Prompt Defense ensures that the prompts are processed securely, protecting the system from such vulnerabilities.
Option A(Secure Data Retrieval and Grounding) relates to ensuring that data used by AI is securely retrieved but does not address prompt security.
Option B(Data Masking) focuses on protecting sensitive information but does not prevent injection attacks.
For more information, refer toSalesforce's Einstein Trust Layer documentationon Prompt Defenseand security features.

Question # 3
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 # 4
Universal Containers (UC) has recently received an increased number of support cases. As a result, UC has hired more customer support reps and has started to assign some of the ongoing cases to newer reps. Which generative AI solution should the new support reps use to understand the details of a case without reading through each case comment?
A. Einstein Copilot
B. Einstein Sales Summaries
C. Einstein Work Summaries


C. Einstein Work Summaries

Explanation:

New customer support reps at Universal Containers can use Einstein Work Summaries to quickly understand the details of a case without reading through each case comment. Work Summaries leverage generative AI to provide a concise overview of ongoing cases, summarizing all relevant information in an easily digestible format.

Einstein Copilot can assist with a variety of tasks but is not specifically designed for summarizing case details.

Einstein Sales Summaries are focused on summarizing sales-related activities, which is not applicable for support cases.

For more details, refer to Salesforce documentation on Einstein Work Summaries.

Question # 5
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


C. Create Close Plan

Explanation: The"Create Close Plan"feature is designed to help sales reps by providing AI-generated strategies and stepsspecifically 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 movedeals 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.

Question # 6
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.


A. In Copilot Builder within the Dynamic Panel, turn on dynamic debugging to show the inputs and outputs.

Explanation: When troubleshooting acopilot custom actionusing flow as the reference action type, enablingdynamic debuggingwithinCopilot Builder's Dynamic Panelis the most effective way to identify the root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both theinputs and outputsof 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.

Question # 7
How should an organization use theEinstein 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.

Question # 8
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.


B. Configure data masking in the Einstein Trust Layer setup.

Explanation: WhenUniversal Containers' AI data masking rulesdo not meet organizational privacy and security standards, the AI Specialist should configure thedata maskingrules within theEinstein Trust Layer. TheEinstein Trust Layerprovides 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 theEinstein 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.

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