An Agentforce configured Data Masking within the Einstein Trust Layer. How should the Agentforce Specialist begin validating that the correct fields are being masked?
Correct Answer: C
To begin validating that the correct fields are being masked inEinstein Trust Layer, the Agentforce Specialist should request theEinstein Generative AI Audit Datafrom theSecurity sectionof the Salesforce Setup menu. This audit data allows the Agentforce 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 Bis correct because it allows for the retrieval of audit data that can be used to validate data masking. * Option A(Flow Debugger) andOption C(Einstein Feedback) do not relate to validating field masking in the context of theEinstein Trust Layer. : Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf. einstein_trust_layer_audit.htm
Question 27
What is the role of the large language model (LLM) in executing an Agent Action?
Correct Answer: B
In Agent, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user's request and determine the correct sequence of actions that should be performed. By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows. The other options are incorrect because: A mentions finding similar requests, which is not the primary role of the LLM in this context. C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM. References: Salesforce Einstein Documentation on Agent Actions Salesforce AI Documentation on Large Language Models
Question 28
Universal Containers' Agent Action includes several Apex classes for the new Agentforce Agent. What is an important consideration when deploying Apex that is invoked by an Agent Action?
Correct Answer: A
Comprehensive and Detailed In-Depth Explanation: Universal Containers (UC) is using Apex classes within an Agent Action for their Agentforce Agent. Deploying Apex in Salesforce has specific requirements, especially when tied to Agentforce functionality. Let' s evaluate the options. * Option A: The Apex classes must have at least 75% code coverage from unit tests, and all dependencies must be in the deployment package.Salesforce enforces a strict requirement that all Apex classes must achieve at least 75% code coverage from unit tests for deployment to production, regardless of their use case (e.g., Agentforce, triggers, or web services). Additionally, when Apex is invoked by an Agent Action (e.g., via a Flow or direct invocation), all dependencies (e.g., referenced classes, objects) must be included in the deployment package to ensure functionality. This is a standard deployment consideration in Salesforce and applies to Agentforce, making this the correct answer. * Option B: Apex classes invoked by an Agent Action may be deployed with less than 75% test coverage as long as the agent is not activated in production.Salesforce's 75% code coverage requirement is mandatory for production deployment, regardless of whether the agent is activated. There's no exemption based on activation status-coverage is enforced at the deployment stage. This option is incorrect and contradicts Salesforce's Apex deployment rules. * Option C: The Apex classes may bypass the 75% code coverage requirement as long as they are only used by the agent.No such bypass exists in Salesforce. The 75% code coverage rule applies universally to all Apex in production, including classes used by Agentforce. Agent-specific usage doesn' t waive this requirement, making this incorrect. Why Option A is Correct: The 75% code coverage requirement and inclusion of dependencies are fundamental Salesforce deployment rules, applicable to Apex in Agent Actions. This ensures reliability and functionality in production, as per official documentation. References: Salesforce Agentforce Documentation: Agent Builder > Custom Actions > Apex- Notes standard Apex deployment rules apply. Salesforce Developer Guide: Apex Testing- Confirms 75% coverage requirement. Trailhead: Deploy Apex Code- Emphasizes coverage and dependencies for production.
Question 29
Universal Container's internal auditing team asks An Agentforce to verify that address information is properly masked in the prompt being generated. How should the Agentforce Specialist verify the privacy of the masked data in the Einstein Trust Layer?
Correct Answer: C
TheAI audit trailin Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the Agentforce Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs. * Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations. * Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes. * Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is processed and masked in AI activities. Reference: "How Salesforce Ensures Trust in AI with Einstein Trust Layer | Salesforce" .
Question 30
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI-driven training content. However, users report that the AI frequently returns outdated documents. Which corrective action should UC implement to improve content relevancy?
Correct Answer: B
Comprehensive and Detailed In-Depth Explanation: UC's issue is that theirfile upload-based Data Library(where PDFs or documents are uploaded and indexed into Data Cloud's vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is toconfigure a custom retriever with a filter(Option B). In Agentforce, a custom retriever allows UC to define specific conditions-such as a filter on a "Last Modified Date" or similar timestamp field-to limit retrieval to documents updated within a recent period (e.g., last 6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source. * Option A: Switching to aKnowledge-based Data Library(using Salesforce Knowledge articles) could work, as Knowledge articles have versioning and expiration features to manage recency. However, this assumes UC's training content is already in Knowledge articles (not PDFs) and requires migrating all uploaded files, which is a significant shift not justified by the question's context. File-based libraries are still viable with proper filtering. * Option B: This is the best corrective action. A custom retriever with a date filter leverages the existing file-based library, refining retrieval without changing the data source, making it practical and targeted. * Option C: Relying on periodic re-uploads with the default retriever is passive and inefficient. It doesn't guarantee recency (old files remain indexed until manually removed) and requires ongoing manual effort, failing to proactively solve the issue. Option B provides a precise, scalable solution to ensure content relevancy in UC's AI-driven training system. : Salesforce Agentforce Documentation: "Custom Retrievers for Data Libraries" (Salesforce Help:https://help. salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5) Salesforce Data Cloud Documentation: "Filter Retrieval for AI" (https://help.salesforce.com/s/articleView? id=sf.data_cloud_retrieval_filters.htm&type=5) Trailhead: "Manage Data Libraries in Agentforce" (https://trailhead.salesforce.com/content/learn/modules /agentforce-data-libraries)
