Universal Containers wants to utilize Agentforce for Sales to help sales reps reach their sales quotas by providing AI-generated plans containing guidance and steps for closing deals. Which feature meets this requirement?
Correct Answer: C
Comprehensive and Detailed In-Depth Explanation: Universal Containers (UC) aims to leverage Agentforce for Sales to assist sales reps with AI-generated plans that provide guidance and steps for closing deals. Let's evaluate the options based on Agentforce for Sales features. * Option A: Create Account PlanWhile account planning is valuable for long-term strategy, Agentforce for Sales does not have a specific "Create Account Plan" feature focused on closing individual deals. Account plans typically involve broader account-level insights, not deal-specific closure steps, making this incorrect for UC's requirement. * Option B: Find Similar Deals"Find Similar Deals" is not a documented feature in Agentforce for Sales. It might imply identifying past deals for reference, but it doesn't involve generating plans with guidance and steps for closing current deals. This option is incorrect and not aligned with UC's goal. * Option C: Create Close PlanThe "Create Close Plan" feature in Agentforce for Sales uses AI to generate a detailed plan with actionable steps and guidance tailored to closing a specific deal. Powered by the Atlas Reasoning Engine, it analyzes deal data (e.g., Opportunity records) and provides reps with a roadmap to meet quotas. This directly meets UC's requirement for AI-generated plans focused on deal closure, making it the correct answer. Why Option C is Correct: "Create Close Plan" is a specific Agentforce for Sales capability designed to help reps close deals with AI- driven plans, aligning perfectly with UC's needs as per Salesforce documentation. References: Salesforce Agentforce Documentation: Agentforce for Sales > Create Close Plan- Details AI-generated close plans. Trailhead: Explore Agentforce Sales Agents- Highlights close plan generation for sales reps. Salesforce Help: Sales Features in Agentforce- Confirms focus on deal closure.
Question 107
What is An Agentforce able to do when the "Enrich event logs with conversation data" setting in Agent is enabled?
Correct Answer: B
When the "Enrich event logs with conversation data" setting is enabled in Agent, it allows An Agentforce or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs. * This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement. * Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality. * Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days. Salesforce Agentforce Specialist References: You can refer to this documentation for further insights: https://help.salesforce.com/s/articleView?id=sf. einstein_copilot_event_logging.htm
Question 108
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 their file 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 to configure 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 a Knowledge-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. References: * 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)
