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| Section | Weight | Objectives |
|---|---|---|
| Einstein Trust Layer & Security | - Data Masking
| |
| Reasoning Engine & AI Actions | - Workflow mastery
| |
| Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
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NEW QUESTION # 130
An Agentforce created a custom Agent action, but it is not being picked up by the planner service in the correct order. Which adjustment should the Al Specialist make in the custom Agent action instructions for the planner service to work as expected?
Answer: A
Explanation:
When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, the Agentforce Specialist must explicitly specify dependent actions using their API names in the custom action's configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements.
Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A's API name must be listed as a dependency in Action B's instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead to race conditions or incorrect execution order.
NEW QUESTION # 131
Which scenario best illustrates the use of Model Context Protocol (MCP) in an enterprise Al deployment?
Answer: B
Explanation:
The Model Context Protocol (MCP) in AgentForce and Salesforce AI architecture enables agents to dynamically discover and connect to external tools or APIs during runtime. The documentation defines it as: "MCP allows LLMs to query registered tool endpoints and retrieve their schemas, enabling dynamic tool discovery and invocation in enterprise AI environments."
NEW QUESTION # 132
Universal Containers (UC) has a library of custom-built personalized investment portfolio APIs, and is planning to extend it to agents. Which method should UC's agent choose to dynamically use the best API service?
Answer: A
Explanation:
The most appropriate and advanced method for an Agentforce agent to dynamically select and use the best API service from a library of custom-built APIs is through Model Context Protocol (MCP) server support (B).
The Model Context Protocol (MCP) is an open standard specifically designed to standardize how AI agents and Large Language Models (LLMs) interact with external tools, systems, and data sources (like custom APIs). An external system, such as a server hosting UC's custom portfolio APIs, can be exposed as an MCP Server. This server provides rich, standardized, human- readable metadata about its "tools" (the APIs it offers). The Agentforce Atlas Reasoning Engine can interpret this metadata to understand the function of each API, the required inputs, and the expected outputs. This allows the agent to dynamically discover, reason over, and select the most appropriate API to execute based on a user's request (e.g., "Show me the best-performing portfolio" vs. "Adjust my risk tolerance").
NEW QUESTION # 133
Universal Containers (UC) is rolling out an AI-powered support assistant to help customer service agents quickly retrieve relevant troubleshooting steps and policy guidelines. The assistant relies on a search index in Data Cloud that contains product manuals, policy documents, and past case resolutions. During testing, UC notices that agents are receiving too many irrelevant results from older product versions that no longer apply. How should UC address this issue?
Answer: C
Explanation:
UC's support assistant uses a Data Cloud search index for grounding, but irrelevant results from outdated product versions are an issue.
NEW QUESTION # 134
Universal Containers wants to automatically populate the Description field on the Account object.
Answer: C
NEW QUESTION # 135
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