素晴らしいAI-201資格専門知識 &合格スムーズAI-201最新受験攻略 |効果的なAI-201ソフトウエア

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AI-201試験に合格して関連認定を取得する場合、試験の準備をするための信頼できる試験ツールを見つける必要があります。それが、AI-201準備ガイドをお勧めしたい理由です。これがあなたが探しているものだと信じているからです。さらに、データ保護法を提供し、AI-201ガイド急流を購入した後、ウイルスの侵入や情報漏えいに悩まされないことをSalesforce保証します。最後になりましたが、ダウンロードと分割払いに関するガイダンスをリモートで提供するSalesforce Certified Agentforce Specialist専門家グループがあります。

Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Cloud & Grounding	20%	- Data integration for AI agents 1. Using Data Cloud for retrieval augmentation 2. Connecting external data sources
Topic 2: Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Testing and validation strategies 2. Sandbox to production deployment
Topic 3: AI Agents	35%	- Agent setup and orchestration 1. Standard and custom agent actions 2. Standard and custom topics - Agent behavior configuration 1. Filters and variables for deterministic behavior 2. Agent reasoning engine concepts
Topic 4: Prompt Engineering	20%	- Prompt Builder usage 1. Prompt templates and configuration 2. Field generation and flex templates - Grounding techniques 1. Best practices for prompt design 2. Data grounding for reducing hallucinations
Topic 5: Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior

>> AI-201資格専門知識 <<

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Salesforce Certified Agentforce Specialist 認定 AI-201 試験問題 (Q152-Q157):

質問 # 152
Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS). What should the team use to access externally-hosted models in the Salesforce Platform?

A. Model Builder
B. Copilot Builder
C. App Builder

正解：A

解説：
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.

質問 # 153
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level. In the score of 1 indicate?

A. The response is the least toxic Einstein Generative AI Toxicity Scoring system, what does a toxicity category.
B. The response is not toxic.
C. The response is the most toxic.

正解：C

解説：
Einstein Trust Layer's Toxicity Scoring categorizes content on a scale of 0 to 1, where 1 indicates the highest level of toxicity (e.g., harmful, biased, or inappropriate language). This scoring helps organizations filter unsafe AI-generated content. A score of 1 triggers mitigation actions, such as blocking the response or alerting administrators.

質問 # 154
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?

A. Predictive model
B. Generative model
C. Predictive model and generative model

正解：A

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 # 155
Universal Containers (UC) wants to improve the productivity of its sales team with generative AI technology. However, UC is concerned that public AI virtual assistants lack adequate company data to general useful responses. Which solution should UC consider?

A. fine-tune the Einstein AI model with CBM data.
B. Enable Agentforce and deploy to sales users.
C. Build Al model with Einstein discovery and deploy to sales users.

正解：A

解説：
Universal Containers (UC) wants to harness generative AI to boost sales productivity. They are wary of public AI virtual assistants (like generic chatbots) that lack sufficient UC-specific data to generate useful business responses.

質問 # 156
Universal Containers' Agent Action includes several Apex classes for the new Agentforce Agent.
What is an important consideration when deploying Apex that is invoked by an Agent Action?

A. The Apex classes must have at least 75% code coverage from unit tests, and all dependencies must be in the deployment package.
B. The Apex classes may bypass the 75% code coverage requirement as long as they are only used by the agent.
C. Apex classes invoked by an Agent Action may be deployed with less than 75% test coverage as long as the agent is not activated in production.

正解：A

解説：
Universal Containers (UC) is using Apex classes within an Agent Action for their Agentforce Agent. Deploying Apex in Salesforce has specific requirements, especially when tied to Agentforce functionality.

質問 # 157
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