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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid |
| Topic 2: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| Topic 3: Prompt Engineering | 20% | - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt template creation: field generation, flex types - Prompt management and user roles |
| Topic 4: Development Lifecycle | 15% | - Deployment: sandbox to production - Testing agents in Testing Center - Monitoring, adoption and optimization - Version control and maintenance |
| Topic 5: AI Agents | 35% | - Agent User security and permissions - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables - Topics and actions configuration - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine |
>> AI-201 Latest Exam Duration <<
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NEW QUESTION # 212
An AgentForce Specialist wants to troubleshoot an agent that is hallucinating weblinks. The agent has an action that uses a prompt template, which is using a knowledge retriever, to generate the output text that the agent will use. Which process is appropriate to find the root cause of the hallucination behavior?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Troubleshooting and Optimization Guide, hallucinations - instances where the agent fabricates details such as weblinks or data - often occur due to issues in prompt construction or retrieved content grounding. The recommended diagnostic process involves inspecting the prompt template instructions and reviewing the resolved prompt output, including the actual retrieved knowledge chunks.
By examining these areas, the AgentForce Specialist can determine whether the hallucinated content originates from ambiguous prompt phrasing, missing grounding variables, or irrelevant retrieval results. This approach ensures an evidence-based investigation directly linked to the agent's reasoning and generation steps.
NEW QUESTION # 213
An administrator wants to check the response of the Flex prompt template they've built, but the preview button is greyed out. What is the reason for this?
Answer: C
Explanation:
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.
NEW QUESTION # 214
An Agentforce is tasked with analyzing Agent interactions looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the AX Specialist to achieve this?
Answer: B
Explanation:
The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the Agentforce Specialist to:
Identify Common Queries: Surface frequently asked questions or recurring issues.
Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.
Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.
NEW QUESTION # 215
Universal Containers needs to create Data Cloud reports to understand agent behavior. Which data lake object (DLO) represents an overarching container capturing contiguous interactions with one or more Al agents?
Answer: A
Explanation:
Per the AgentForce Data Cloud Integration Guide, the AI Agent Session (AIAGENTSESSION) object represents an overarching container that tracks a continuous interaction between one or more AI agents and a user. The document describes: "AI Agent Session is the parent container for a contiguous set of AI Agent interactions. It captures metadata, start and end times, and the relationship to individual messages exchanged during the session."
NEW QUESTION # 216
What is the importance of Action Instructions when creating a custom Agent action?
Answer: B
Explanation:
In Salesforce Agentforce, custom Agent actions are designed to enable AI-driven agents to perform specific tasks within a conversational context. Action Instructions are a critical component when creating these actions because they define the expected user experience by outlining how the action should behave, what it should accomplish, and how it interacts with the end user.
These instructions act as a blueprint for the action's functionality, ensuring that it aligns with the intended outcome and provides a consistent, intuitive experience for users interacting with the agent. For example, if the action is to "schedule a meeting," the Action Instructions might specify the steps (e.g., gather date and time, confirm with the user) and the tone (e.g., professional, concise), shaping the user experience.
NEW QUESTION # 217
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