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Salesforce AI-201 Exam Syllabus Topics:
| Section |
Weight |
Objectives |
| Topic 1: Development Lifecycle |
15% |
- Testing agents in Testing Center - Monitoring, adoption and optimization - Version control and maintenance - Deployment: sandbox to production
|
| Topic 2: Data Cloud for Agentforce |
20% |
- Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Agentforce Data Library types
|
| Topic 3: Prompt Engineering |
20% |
- Prompt template creation: field generation, flex types - Prompt management and user roles - Grounding techniques and best practices - Prompt Builder usage and use cases
|
| Topic 4: AI Agents |
35% |
- Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Agent User security and permissions - Topics and actions configuration
|
| Topic 5: Multi-Agent Interoperability |
5% |
- Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios
|
>> AI-201技術内容 <<
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Salesforce Certified Agentforce Specialist 認定 AI-201 試験問題 (Q54-Q59):
質問 # 54
The sales team at a hotel resort would like to generate a guest summary about the guests' interests and provide recommendations based on their activity preferences captured in each guest profile. They want the summary to be available only on the contact record page. Which AI capability should the team use?
- A. Prompt Builder
- B. Model Builder
- C. Agent Builder
正解:A
解説:
The hotel resort team needs an AI-generated guest summary with recommendations, displayed exclusively on the contact record page.
質問 # 55
Cloud Kicks wants to integrate its agent with its custom website. The goal is for customers to interact with the custom agent chat interface. Which approach provides the framework for the custom web application to communicate with the agent?
- A. Model Context Protocol (MCP)
- B. Agent-to-Agent (A2A)
- C. Agent API
正解:C
解説:
The AgentForce API Integration Guide defines the Agent API as the framework that enables external web or mobile applications to communicate directly with Salesforce-hosted agents. This API supports message exchange, session management, and context persistence -- allowing developers to build custom chat interfaces while maintaining secure, real-time connectivity with the AgentForce reasoning engine.
質問 # 56
What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?
- A. Determine a user's topic access and sort actions by priority to be executed.
- B. Identify the best matching topic and actions and correct order of execution.
- C. Find similar requested topics and provide the actions that need to be executed.
正解:B
解説:
In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions.
質問 # 57
Universal Containers needs its sales reps to be able to only execute prompt templates. What should the company use to achieve this requirement?
- A. Prompt Execute Template permission set
- B. Prompt Template User permission set
- C. Prompt Template Manager permission set
正解:B
解説:
Salesforce Agentforce leverages Prompt Builder, a powerful tool that allows administrators to create and manage prompt templates, which are reusable frameworks for generating AI-driven responses. These templates can be invoked by users to perform specific tasks, such as generating sales emails or summarizing records, based on predefined instructions and grounded data. In this scenario, Universal Containers wants its sales reps to have the ability to only execute these prompt templates, meaning they should be able to run them but not create, edit, or manage them.
質問 # 58
Universal Containers wants to reduce overall customer support handling time by minimizing the time spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields. Which combination of Agentforce for Service features enables this effort?
- A. Einstein Reply Recommendations and Case Summaries
- B. Einstein Service Replies and Work Summaries
- C. Einstein Reply Recommendations and Case Classification
正解:A
解説:
Universal Containers (UC) aims to streamline customer support by addressing two goals:
reducing in-chat typing time for routine answers and minimizing post-chat analysis by auto- suggesting case field values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification (Option A) are the ideal combination to achieve this.
Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to- use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC's first goal.
Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC's second goal.
質問 # 59
......
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