Eliminate Risk of Failure with Salesforce-AI-Specialist Exam Dumps

Prompt Defense is a feature in the Einstein Trust Layer that helps minimize the risks of jailbreaking and prompt 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 to Salesforce's Einstein Trust Layer documentation on Prompt Defense and security features.

The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post for new product patches. Prompt Builder allows the AI Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.

Option B (agent performance score) would likely involve predictive modeling, not prompt generation.

Option C (estimating support ticket volume) would require data analysis and predictive tools, not prompt building.

For more details, refer to Salesforce's Prompt Builder documentation for generative AI content creation.

Before creating a Field Generation prompt template, the AI Specialist must ensure that the Salesforce org is set to API version 59 or higher. This version of the API introduces support for advanced generative AI features, such as enabling fields for generative AI outputs. This is a critical technical requirement for the Field Generation prompt template to function correctly.

Option A (rich text field requirement) is not necessary for generative AI functionality.

Option C (Dynamic Forms) does not impact the ability of a field to be generative AI-enabled, although it might enhance the user interface.

For more information, refer to Salesforce documentation on API versioning and Field Generation templates.

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 B is 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.

Salesforce Model Builder Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.model_builder_use_cases.htm

To understand multiple real-world user utterances and ensure the correct action is selected before activating a custom copilot action, the recommended tool is Copilot Builder. This tool allows AI Specialists to design and test conversational actions in response to user inputs, helping ensure the copilot can accurately handle different user queries and phrases. Copilot Builder provides the ability to test, refine, and improve actions based on real-world utterances.

Option C is correct as Copilot Builder is designed for configuring and testing conversational actions.

Option A (Model Playground) is used for testing models, not user utterances.

Option B (Einstein Copilot) refers to the conversational interface but isn't the right tool for designing and testing actions.

Salesforce Copilot Builder Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_builder.htm