AI Associate Salesforce-AI-Specialist Syllabus Exam Questions Answers

When creating acustom prompt templateto populate a field with generated output, the most appropriate template type isField Generation. This template is specifically designed for generating field-specific outputs using generative AI.

Additionally, the AI Specialist must ensure thatDynamic Fieldsare enabled.Dynamic Fieldsallow the system to use real-time data inputs from related records or fields when generating content, ensuring that the AI output is contextually accurate and relevant. This is crucial when populating specific fields with AI-generated content, as it ensures the data source remains dynamic and up-to-date.

TheEinstein Trust Layerwill track and audit the interactions to ensure the organization can monitor AI adoption and make necessary enhancements based on AI usage patterns.

For further reading, refer to Salesforce’s guidelines onField Generation templatesand theEinstein Trust Layer.

To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):

Create the Serving Endpoint in Databricks:

The serving endpoint is necessary to make the trained model available for real-time inference. Databricks provides tools to host and expose the model via an endpoint.

Configure the Model Using Model Builder:

After creating the endpoint, the AI Specialist should configure it within Einstein Studio'sModel Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.

Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.

Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.

To create a digest of account action plans using the generative API feature,Universal Containersshould use theREST API. TheREST APIis ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.

Metadata APIis used for retrieving and deploying metadata, not for data operations like generating summaries.

SOAP APIis an older API used for integration but is less flexible compared to REST for this specific use case.

For more details, refer toSalesforce REST API documentationregarding using REST for data integration and generating content.

TheEinstein Trust Layeris 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: Theaudit trailcaptures 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. TheData Cloudprovides the infrastructure for managing and accessing this audit data.

Why not B?UsingPrompt Builderin 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?Although 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 theSalesforce AI Trust Layerdocumentation, which outlines how organizations can manage and track generative AI interactions securely.