Agentforce-Specialist Salesforce Certified Agentforce Specialist (AI-201) Free Practice Exam Questions (2025 Updated)

To integrate web activity data from Data Cloud into a prompt template, the correct approach is to enrich the Contact object with the activity records as a related list and use related list grounding (Option B). Here’s why:

Data Cloud Integration: Data Cloud unifies web activity data and associates it with the unified Contact record. By adding these activities as a related list to the Contact, the data becomes accessible to the prompt template.

Prompt Template Grounding: Salesforce prompt templates support grounding on related records. When the Contact is passed to the prompt template, the template can reference the related web activity records (via the related list) to extract insights.

Structured Data Handling: This method aligns with Salesforce best practices for grounding, ensuring the large language model (LLM) receives structured, context-rich data without overwhelming it with raw activity lists.

Why Other Options Are Incorrect:

A. Calling the prompt directly from Data Cloud: Prompt templates are invoked within Salesforce, not directly from Data Cloud. Grounding requires associating data with Salesforce objects, not ad-hoc web activity inclusion.

C. Passing a list of activity records as input: While technically possible, this bypasses Salesforce’s grounding framework, which relies on object relationships. It also risks exceeding LLM input limits and lacks scalability.

Salesforce Data Cloud Implementation Guide: Explains how to enrich standard/custom objects with related data for AI use cases.

Prompt Template Documentation: Highlights grounding on related lists to leverage contextual data for LLM prompts.

Trailhead Module: "Einstein Prompt Builder Basics" demonstrates grounding techniques using related records.

The task requires analyzing user inputs, requests, and queries to identify patterns and trends in Agentforce interactions. Let’s assess the options based on Agentforce’s analytics capabilities.

Option A: Agent Event Logs dashboard.Agent Event Logs capture detailed technical events (e.g., API calls, errors, or system-level actions) related to agent operations. While useful for troubleshooting or monitoring system performance, they are not designed to analyze user inputs or conversational trends. This option does not meet the requirement and is incorrect.

Option B: AI Audit and Feedback Data dashboard.There’s no specific "AI Audit and Feedback Data dashboard" in Agentforce documentation. Feedback mechanisms exist (e.g., user feedback on responses), and audit trails may track changes, but no single dashboard combines these for analyzing user queries and trends. This option appears to be a misnomer and is incorrect.

Option C: User Utterances dashboard.The User Utterances dashboard in Agentforce Analytics is specifically designed to analyze user inputs, requests, and queries. It aggregates and visualizes what users are asking the agent, identifying patterns (e.g., common topics) and trends (e.g., rising query types). Specialists can use this to refine agent instructions or topics, making it the perfect tool for this task. This is the correct answer per Salesforce documentation.

Why Option C is Correct:

The User Utterances dashboard is tailored for conversational analysis, offering insights into user interactions that align with the specialist’s goal of identifying patterns and trends. It’s a documented feature of Agentforce Analytics for post-deployment optimization.

When rolling out a new Generative AI initiative in Salesforce using Prompt Builder, it’s important to understand its current limitations. One key limitation is that changes to prompt templates (creation, edits, or deletions) are not logged in the Setup Audit Trail, which means admins won’t have a historical record of modifications for compliance or troubleshooting.

Comprehensive and Detailed Explanation From Exact Extract:

The guide on RAG and search indexes indicates that if you need fine‑tuned retrieval behaviour (such as filtering by product line and ranking by recency), you should build a custom retriever. The documentation states: “You can add ranking factors such as recency and popularity at the time of index creation … Use pre‑filter fields and ranking factors.” Also: “When you create a search index, Data 360 automatically creates a default retriever … you can create custom retrievers in Einstein Studio to refine search criteria.” Hence to satisfy filtering by product line and recency ranking, the correct answer is B. Option A (default retriever) does not guarantee the filter/ranking customization; Option C (semantic embeddings with default metadata filters) may offer some filter capability but doesn’t explicitly provide ranking by recency and fine filter by product line. Thus B is correct.

Agent is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Agent analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface.

Option C accurately describes this process. Agent does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses, facilitating a broad range of user requests.

Why is "Automates Ingestion, Indexing, and Default Retriever Creation" the correct answer?

An Agentforce Data Library is a key component in ensuring that an AI agent provides precise and up-to-date responses by:

✔ Automating data ingestion → Brings in data from various sources.

✔ Indexing the data → Organizes it efficiently for AI retrieval.

✔ Creating a default retriever → Enables the AI to fetch relevant data dynamically when answering customer queries.

Key Features of an Agentforce Data Library:

Automates Data Ingestion

Integrates real-time and historical data into Salesforce Data Cloud.

Ensures that relevant updates are continuously fed into the AI system.

Indexes Data for Efficient Retrieval

Enhances searchability for quick, context-aware responses.

Enables fast AI response times while maintaining accuracy.

Creates a Default Retriever

AI agents use the retriever to fetch the most relevant and current information.

The retriever grounds AI-generated responses using structured and indexed data.

Why Not the Other Options?

❌ A. Automates ingestion, taxonomical classification, and precision keyword search retrieval

Incorrect because Agentforce does not rely on keyword searches but on indexing and AI-driven retrieval.

❌ C. Automates ingestion and OCR processing of PDFs

Incorrect because OCR (Optical Character Recognition) is not the primary function of an Agentforce Data Library.

AI grounding is based on indexed and structured data, not raw OCR-extracted text.

Agentforce Specialist References

Salesforce AI Specialist Material explains that Agentforce Data Libraries automate data ingestion, indexing, and retriever setup for AI-powered responses​.

Salesforce Instructions for Certification confirm that AI responses are grounded in structured and indexed Data Libraries.

Agent currently supports various actions like creating and updating records but does not support the Delete Record action. Therefore, the user's request to delete activities from the past 7 days cannot be fulfilled using Agent.

Unsupported Action: The inability to delete records is due to the current limitations of Agent's supported actions. It is designed to assist with tasks like data retrieval, creation, and updates, but for security and data integrity reasons, it does not facilitate the deletion of records.

User Permissions: Even if the user has the necessary permissions to delete records within Salesforce, Agent itself does not have the capability to execute delete operations.

According to the AgentForce Retrieval Optimization Guide, when users ask both structured (exact) and unstructured (natural language) questions, the best practice is to use hybrid search. The documentation states:

“Hybrid search combines the precision of keyword retrieval for structured terms, such as IDs or model numbers, with the semantic flexibility of vector search for natural language queries. This approach ensures both deterministic accuracy and contextual understanding.”

Option A (keyword search only) fails for natural language queries, which require semantic understanding.

Option B (semantic search only) may misinterpret or overlook structured identifiers like product or model numbers.

Therefore, Option C—hybrid search—provides the ideal balance between exact match precision and contextual recall.

References (AgentForce Documents / Study Guide):

AgentForce Retrieval and Indexing Guide: “Choosing Between Keyword, Semantic, and Hybrid Search”

AgentForce Data Cloud Handbook: “Optimizing Multi-Modal Retrieval Strategies”

AgentForce Study Guide: “Hybrid Search for Structured and Unstructured Queries”