Salesforce Updated Agentforce-Specialist Exam Questions and Answers by nicole

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 Agentforce 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.

Comprehensive and Detailed In-Depth Explanation:UC is deploying an Agentforce Service Agent with multiple topics and actions to production. Let’s assess deployment considerations.

Option A: Deploy agent components without a test run in staging, relying on production data for reliable results. Sandbox configuration alone ensures seamless production deployment.Skipping staging tests is risky and against best practices. Sandbox configuration doesn’t guarantee production success without validation, making this incorrect.

Option B: Ensure all dependencies are included, Apex classes meet 75% test coverage, and configuration settings are aligned with production. Plan for version management and post-deployment activation.This is a comprehensive approach: dependencies (e.g., flows, Apex) must be deployed, Apex requires 75% coverage, and production settings (e.g., permissions, channels) must align. Version management tracks changes, and post-deployment activation ensures controlled rollout. This aligns with Salesforce deployment best practices for Agentforce, making it the correct answer.

Option C: Deploy flows or Apex after agents, topics, and Agent Actions to avoid deployment failures and potential production agent issues requiring complete redeployment.Deploying components separately risks failures (e.g., actions needing flows failing). All components should deploy together for consistency, making this incorrect.

Why Option B is Correct:Option B covers all critical deployment considerations for a robust Agentforce rollout, as per Salesforce guidelines.

References:

Salesforce Agentforce Documentation: Deploy Agents to Production – Lists dependencies and coverage.

Trailhead: Deploy Agentforce Agents – Emphasizes testing and activation planning.

Salesforce Help: Agentforce Deployment Best Practices – Confirms comprehensive approach.

When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.

Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality.

Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.

Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.

Salesforce Agentforce Specialist References:Refer to the documentation on troubleshooting Flex templates here: https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm

Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud’s vector database or Salesforce records) to ground AI responses. UC’s issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever’s search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the ‘Policy’ category” or “records created after a certain date”) that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC’s problem effectively.

Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.

Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC’s goal of improving relevance.

Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.

Option A is the most effective step to ensure relevance in retrieved data.

References:

Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm &type=5)

Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_retrieval_filters.htm &type=5)