Exactprep Agentforce-Specialist Questions

Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:

According to the official AgentForce implementation guidelines and RAG (Retrieval-Augmented Generation) architecture within Salesforce, maintaining retrieval accuracy depends on ensuring that embeddings and indexed content remain synchronized with the most recent data. When product documentation or knowledge base content changes, the underlying text used for vector embeddings must also be updated to reflect the new information.

The AgentForce documentation clearly specifies that when content is modified, the recommended practice is to rebuild the search index. This process regenerates the document chunks, re-embeds them using the latest model, and updates the index used by the retrieval system. This ensures that queries return the most current and relevant responses aligned with the updated content.

Leaving embeddings unchanged (Option A) would cause retrievals to surface outdated or irrelevant information, as the underlying semantic representations would no longer match the source material. Similarly, manually deleting stale data chunks (Option C) does not ensure a full refresh of vector data and can lead to incomplete or inconsistent results.

Therefore, as per AgentForce best practices, the correct approach is Option B – Rebuild the search index, ensuring that all embeddings, chunks, and indexed data are aligned with the latest version of the content.

Universal Containers (UC) needs a solution that can automatically qualify and nurture thousands of new leads 24/7, provide accurate and consistent responses to prospects, schedule meetings for qualified leads, and allow sales representatives to focus on relationship building and closing deals. These needs align precisely with the Agentforce SDR Agent.

According to official AgentForce documentation, “Agentforce SDR helps sales teams qualify and nurture leads at scale, around the clock. It acts as a digital sales development representative capable of engaging new leads instantly, asking the right qualifying questions, answering inquiries accurately using connected Salesforce data, and automatically scheduling meetings on behalf of the sales team.”

The documentation further explains that the SDR Agent is designed to “personalize outreach, manage follow-up sequences, and book meetings directly from your website or campaign pages.” This automation “frees your human reps to focus on high-value interactions and closing opportunities rather than manual lead qualification.”

By contrast, the Sales Coach capability focuses on guiding and coaching sales representatives internally rather than interacting with prospects, and the Commerce Agent is designed for e-commerce use cases such as assisting shoppers with product discovery and order management—not lead nurturing.

References (AgentForce Documents / Study Guide):

AgentForce SDR Overview – Salesforce AgentForce Documentation

AgentForce for Sales – SDR Agent Use Cases

AgentForce Study Guide: “Qualify and Nurture Leads at Scale with SDR Agents”

Salesforce Trailhead: “Get to Know AgentForce SDR”

The Prompt Builder and Data Cloud Integration Guide explains that Data Cloud information can be accessed directly through Data Cloud related lists or prompt-initiated flows, which fetch relevant data dynamically. The documentation states: “Prompt Builder supports retrieving Data Cloud data using related lists for contextual grounding or invoking flows that query Data Cloud objects at runtime. This enables AI prompts to generate summaries, recommendations, or insights directly from unified customer profiles.”

Option A is incorrect because direct access to data model objects (DMOs) in Flex templates is not supported in Prompt Builder. Option C (external APIs) is unnecessary, as Prompt Builder has native integration with Data Cloud.

Thus, Option B is the correct and Salesforce-documented method to access Data Cloud data from Prompt Builder.

References (AgentForce Documents / Study Guide):

Salesforce Prompt Builder Guide: “Integrating with Data Cloud”

AgentForce Study Guide: “Fetching Data Cloud Data with Prompt-Initiated Flows”

Salesforce Data Cloud Documentation: “Using Related Lists in Prompt Templates”

The AgentForce Testing Center Guide specifies that all testing activity—including prompt tests, structured batch runs, and evaluation sessions—consumes AI credits, which can be monitored directly through the Testing Center interface. The documentation states: “Testing Center provides visibility into testing performance, outcomes, and credit usage. Administrators can view test credit consumption metrics to understand resource utilization across agents and environments.”

Agentforce Analytics (Option A) provides insights into performance, engagement, and operational metrics but does not directly track credit consumption tied to testing. Digital Wallet (Option B) manages AI credit balances across orgs but does not offer test-specific tracking. Therefore, Testing Center is the correct answer since it explicitly supports monitoring credit usage during agent testing activities.

References (AgentForce Documents / Study Guide):

AgentForce Testing Center Guide: “Monitoring Credit Consumption During Testing”

AgentForce Admin Handbook: “Tracking Usage and Test Costs”

AgentForce Study Guide: “Testing Resource Visibility and Credit Management”