P.S. JapancertがGoogle Driveで共有している無料かつ新しいAI-201ダンプ：https://drive.google.com/open?id=1Ij7ZIJWqTld8nAZw6DiWRi9tSbLAxuvm
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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Reasoning Engine & AI Actions | - Workflow mastery
| |
| Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
| Einstein Trust Layer & Security | - Data Masking
|
JapancertのSalesforceのAI-201「Salesforce Certified Agentforce Specialist」試験トレーニング資料はあなたがリスクフリー購入することを保証します。購入する前に、あなたはJapancertが提供した無料な一部の問題と解答をダウンロードして使ってみることができます。Japancertの問題集の高品質とウェブのインタ—フェ—スが優しいことを見せます。それに、我々は一年間の無料更新サービスを提供します。失敗しましたら、当社は全額で返金して、あなたの利益を保障します。Japancertが提供した資料は実用性が高くて、絶対あなたに向いています。
質問 # 181
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment. Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
正解：B
解説：
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision- making process.
Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
質問 # 182
Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein's generative AI service.
- No data is used for LLM training or product improvements by third-
party LLMs.
- No data is retained outside of UC's Salesforce org.
- The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should the Agentforce Specialist highlight to UC that addresses these requirements?
正解：A
解説：
Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data.
The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:
No data is used for training or product improvements by third-party LLMs.
No data is retained outside of the customer's Salesforce organization.
The LLM provider cannot access any customer data.
This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging generative AI capabilities.
Prompt Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.
質問 # 183
When creating a custom retriever in Einstein Studio, which step is considered essential?
正解：B
解説：
In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:
Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
質問 # 184
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production. Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?
正解：B
解説：
Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
The Agentforce Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.
What Can Actually Be Deployed from Sandbox to Production?
Custom Fields: Metadata that is easily created in sandbox and then deployed.
Quick Actions: Also metadata-based and can be deployed from sandbox to production.
Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.
質問 # 185
Which element in the Omni-Channel Flow should be used to connect the flow with the agent?
正解：B
解説：
UC is integrating an Agentforce agent with Omni-Channel Flow to route work.
質問 # 186
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P.S.JapancertがGoogle Driveで共有している無料の2026 Salesforce AI-201ダンプ：https://drive.google.com/open?id=1Ij7ZIJWqTld8nAZw6DiWRi9tSbLAxuvm