AI-201学習資料、AI-201受験体験

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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Einstein Trust Layer & Security		- Trust Layer & Security Features 1. Enterprise-grade deployment - Data Masking 1. Configuration of privacy data entities
Reasoning Engine & AI Actions		- Workflow mastery 1. Standard Agent actions 2. Custom Agent actions (Apex) 3. Custom Agent actions (Flows)
Prompt Engineering	30%	- Agentforce AI Setup & Configuration 1. Advanced implementation strategies - Prompt Builder & Template Creation 1. Best practices
Agent Force Concepts	30%	- Einstein GPT Integration 1. Advanced implementation strategies - Data Cloud & Grounding Techniques 1. Real-world applications

>> AI-201学習資料 <<

AI-201受験体験 & AI-201試験攻略

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Salesforce Certified Agentforce Specialist 認定 AI-201 試験問題 (Q168-Q173):

質問 # 168
A Salesforce Administrator is exploring the capabilities of Agent to enhance user interaction within their organization. They are particularly interested in how Agent processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Agent directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users. How does Agent handle user requests In Salesforce?

A. Agent analyzes the user's request and LLM technology is used to generate and display the appropriate response.
B. Agent will trigger a flow that utilizes a prompt template to generate the message.
C. Agent will perform an HTTP callout to an LLM provider.

正解：A

解説：
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.

質問 # 169
Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management. For security purposes, UC wants the agent to verify a customer's identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed. Which approach best meets this requirement?

A. Create a variable to store the verification status, set it as output from a "Verify Identity" action, and apply a filter so any case-related actions only run when the variable confirms verification.
B. Store the verification status in a custom variable and set a global instruction that the agent should check this variable before sharing case information.
C. Use keywords such as "Always" and "Never" to write clear logic in Topic Instructions to verify user identity before providing any case information,

正解：A

解説：
The AgentForce for Service Implementation Guide clearly outlines that when an agent must verify identity before performing any case-related operations, the correct method is to use a variable- based control flow. The documentation specifies: "To maintain deterministic and secure behavior, define a variable (for example, `isVerified') that stores the result of an identity verification step.
Use this variable as a conditional filter in the topic flow to ensure that case-related actions execute only when the variable equals `true'." This ensures that no sensitive or case-specific data is shared unless verification is explicitly confirmed. It provides a deterministic safeguard, as the system only proceeds with case data actions after the verification variable confirms completion.

質問 # 170
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?

A. Data Masking
B. Prompt Defense
C. Secure Data Retrieval and Grounding

正解：B

解説：
The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.
Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.

質問 # 171
At Universal Containers, a sales manager is tackling a tough challenge as several new junior sales reps struggle with objection handling and price negotiations for complex deals. The manager lacks the time to personally guide each sales rep through their specific customer scenarios before their critical meetings. The junior sales reps have asked for a tool that would allow them to practice their pitches by simulating tough conversations and receive personalized feedback that is specific to the commerce opportunity they are working on. Which Salesforce solution should an Agentforce Specialist recommend?

A. Sales Coach
B. SDR Agent
C. Employee Coach

正解：A

解説：
The AgentForce for Sales Overview defines Sales Coach as the AI-powered solution designed to help sales professionals practice and improve selling skills. The guide describes: "Sales Coach simulates customer interactions, allows reps to role-play objection handling, and provides personalized feedback based on real opportunity data." This directly matches the scenario where sales reps want to practice negotiation and objection handling with scenario-specific feedback.

質問 # 172
An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client. How should the Agentforce Specialist integrate the custom LLM into Salesforce?

A. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
B. Add the fine-tuned LLM in Einstein Studio Model Builder.
C. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.

正解：B

質問 # 173
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