Salesforce Agentforce Specialist Online Practice Questions

Explanation:
Context of the Question
Universal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field.
Field Generation Prompt Template
Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.
Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.
Why Not Flex or Sales Email Prompt Templates?
Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.
Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting outreach or follow-up messages). This template is not specifically built to populate a field on a record.
Conclusion
For automatically populating the Description field with AI-generated content, the Field Generation prompt template (Option A) is the correct choice.
Salesforce Agentforce Specialist Reference & Documents
Salesforce Documentation: Prompt Template Types
Explains various template types (Field Generation, Flex, Email, etc.) and their typical use cases.
Salesforce Agentforce Specialist Study Guide
Highlights Field Generation prompt templates for populating or updating record fields with AI-generated text.

Explanation:
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses. Key considerations:
Field Types: Standard Service AI Grounding supports String and Text Area fields. Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct.
Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object.
Visibility: Service AI Grounding respects user permissions and sharing settings unless overridden (Option C is incorrect).
Reference: Salesforce Help: Service AI Grounding Requirements
Explicitly states support for "Text Area and String fields" only.

Explanation:
Per the AgentForce Testing Center Operations Guide, the batch testing feature allows organizations to upload structured CSV test templates containing real-world user utterances, expected classifications, and desired outcomes. Once uploaded, these test cases are executed in bulk within the Testing Center to validate agent accuracy, reasoning consistency, and grounding before activation.
This structured approach supports repeatable, scalable testing and ensures objective evaluation across multiple topics and scenarios.
Option B (manual testing) is suitable for early prototyping but not scalable or auditable.
Option C (relying on pilot feedback) occurs post-deployment and risks exposing users to unverified behavior.
Therefore, the recommended approach is Option A C Upload a structured CSV test template and run batch test cases in Testing Center.
Reference: AgentForce Testing Guide ― “Using CSV Test Templates for Automated Batch Evaluation.”

Explanation:
According to the Salesforce Prompt Builder Best Practices Guide, an effective prompt must include Role, Task, Context, Constraints, and Format clearly defined ― a structure known as the RTCCF model. The documentation explains: “Prompts should specify the assistant’s role, define a clear task, include context and constraints, and provide output format instructions to ensure predictable and high-quality responses.”
Option A follows this framework precisely. It defines:
Role: The assistant’s identity (“experienced support agent”).
Task: Summarizing case notes.
Context: Customer issue, troubleshooting steps, next actions.
Constraints: Limit of 5 sentences, plain language, include “No next action required” if applicable.
Format: Numbered sentences for clarity.
Options B and C omit critical prompt design elements such as strict constraints or output formatting and therefore do not align with Salesforce’s prompt design standard.
Reference (AgentForce Documents / Study Guide):
Salesforce Prompt Builder Guide: “Prompt Structure Using RTCCF Model”
AgentForce Prompt Template Design Guide: “Best Practices for Summarization Prompts”
Salesforce AI Prompt Engineering Study Guide

Explanation:
The AgentForce Retrieval Configuration Guide outlines that when external data must be filtered by specific criteria―such as WarrantyStatus = Active―the correct approach is to use Hybrid Search with pre-filtering logic defined in a custom retriever. The documentation specifies: “Hybrid search allows combining keyword precision and semantic context. When used with a custom retriever, administrators can pre-filter data on metadata fields (e.g., WarrantyStatus) to ensure that only eligible records are returned.”
Option A (relying on instructions) introduces non-deterministic behavior because the LLM cannot enforce data constraints.
Option B (custom variable filtering) applies post-retrieval filtering, which is less efficient and less secure than index-level filtering. Therefore, Option C aligns with Salesforce’s best practice for deterministic, metadata-based retrieval control.
Reference (AgentForce Documents / Study Guide):
AgentForce Data Cloud Retrieval Guide: “Building Custom Retrievers with Metadata Filters”
Einstein Studio for AgentForce: “Hybrid Search Pre-Filtering”
AgentForce Study Guide: “Filtering Warranty Data by Active Status”