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Salesforce Salesforce-AI-Specialist Exam Sample Questions

Question # 1

An AI Specialist configured Data Masking within the Einstein Trust Layer. How should the AI Specialist begin validating that the correct fields are being masked?

A. Use a Flow-based resource in Prompt Builder to debug the fields’ merge values using Flow Debugger.

B. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.

C. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.

Answer

B. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.

Explanation:

To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu. This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.

Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.

Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.

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Question # 2

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

Answer

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.

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Question # 3

Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the "Save As" function. However,UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters. What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?

A. Use Model Playground to create a model configuration with the specified parameters.

B. Manually add the hyperparameters to the new template.

C. Revert to using the standard template without modifications.

Answer

B. Manually add the hyperparameters to the new template.

Explanation: WhenUniversal Containerscreates a new Sales Email prompt template using the"Save As"function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the AI Specialist shouldmanually add the necessary hyperparametersto the new template.
Hyperparameters likeTemperature,Frequency Penalty, andPresence Penaltydirectly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.
Option A (Model Playground)is not necessary here, as it focuses on fine-tuning models, not adjusting templates directly.
Option C (Reverting to the standard template)does not solve the issue of customizing the prompt template.
For more information, refer toPrompt Builder documentationon configuring hyperparameters in custom templates.

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Question # 4

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 beapplicable for Einstein AI todraft accurate email responses. Which solution should UC use to ensure Einstein AI can draft responsesfrom a defined data source?

A. Service AI Grounding

B. Work Summaries

C. Service Replies

Answer

A. Service AI Grounding

Explanation: Service AI Groundingis the solution thatUniversal Containersshould use to ensureEinstein AIdrafts 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 asKnowledge articlesorcases.
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 SummariesandService Repliesare useful features but do not address the need for grounding AI outputs in specific, current data sources likeService AI Groundingdoes.
For more details, you can refer to Salesforce’sService AI Grounding documentationfor managing AI-generated content based on accurate data sources.

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Question # 5

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.

Answer

C. Run Einstein Copilot Analytics.

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.

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