AI-201問題サンプル & AI-201最新試験

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Salesforce AI-201 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Reasoning Engine & AI Actions- Workflow mastery
  • 1. Custom Agent actions (Apex)
  • 2. Standard Agent actions
  • 3. Custom Agent actions (Flows)
Topic 2: Prompt Engineering30%- Prompt Builder & Template Creation
  • 1. Best practices
- Agentforce AI Setup & Configuration
  • 1. Advanced implementation strategies
Topic 3: Agent Force Concepts30%- Data Cloud & Grounding Techniques
  • 1. Real-world applications
- Einstein GPT Integration
  • 1. Advanced implementation strategies
Topic 4: Einstein Trust Layer & Security- Data Masking
  • 1. Configuration of privacy data entities
- Trust Layer & Security Features
  • 1. Enterprise-grade deployment

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最新のAI-201問題サンプル試験-試験の準備方法-正確的なAI-201最新試験

AI-201試験は難しいです。だから、AI-201復習教材を買いました。本当に助かりました。先月、AI-201試験に参加しました。今日は、試験の結果をチエックし、嬉しいことに、AI-201試験に合格しました。AI-201復習教材は有効的な資料です。

Salesforce Certified Agentforce Specialist 認定 AI-201 試験問題 (Q293-Q298):

質問 # 293
What is the role of the large language model (LLM) in executing an Agent Action?

正解：C

解説：
In Agent, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user's request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.


質問 # 294
What is the primary function of the reasoning engine in Agentforce?

正解：A

解説：
Why is "Identifying agent topics and actions to respond to user utterances" the correct answer?
In Agentforce, the reasoning engine plays a critical role in interpreting user queries and determining the appropriate agent response.
Key Functions of the Reasoning Engine in Agentforce:
Analyzing User Intent
The reasoning engine interprets the meaning behind natural language user inputs.
It maps user utterances to predefined topics to determine the correct AI-generated response.
Selecting the Appropriate Agent Action
The engine evaluates available actions and selects the best response based on the detected topic.
For example, if a user asks, "What is my current account balance?", the reasoning engine:
Identifies the topic: "Account Information"
Chooses the correct action: "Retrieve account balance"
Executes the action and returns the response
Ensuring AI Accuracy and Context Awareness
The reasoning engine grounds AI-generated responses in relevant Salesforce data, ensuring accurate outputs.


質問 # 295
Coral Cloud Resorts (CCR) wants to configure its agent so that booking actions are only available when a customer's membership tier is "Premium" or "Elite". This business rule must be enforced deterministically. What should CCR implement?

正解：C

解説：
Per the AgentForce Configuration and Control Flow Guide, enforcing deterministic business rules
- such as restricting certain actions based on a data condition-requires using context variables with conditional filters. The guide specifies: "Use context variables mapped to relevant Salesforce fields to store state information. Then apply conditional filters to ensure actions execute only when specific conditions (e.g., membership tier) are met." This ensures the rule is deterministic, meaning the action cannot trigger if the condition is not satisfied.


質問 # 296
Universal Containers plans to enhance the customer support team's productivity using AI. Which specific use case necessitates the use of Prompt Builder?

正解：C

解説：
The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post for new product patches. Prompt Builder allows the Agentforce Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.


質問 # 297
How should an organization use the Einstein Trust layer to audit, track, and view masked data?

正解：B

解説：
The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce's AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:
Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.


質問 # 298
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