AI-201試験関連情報、AI-201テキスト

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Salesforce AI-201 Exam Syllabus Topics:

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

>> AI-201試験関連情報 <<

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

質問 # 293
Universal Containers (UC) users are complaining that agent answers are not satisfactory. The agent is using PDF files as a knowledge source. How should UC troubleshoot this issue?

正解：B

解説：
Why is "Verify the retriever's filter criteria and data source connection" the correct answer?
If agent answers are not satisfactory when using PDF files as a knowledge source, the issue is likely caused by:
Retriever misconfiguration
If filters are too broad or too restrictive, AI may fail to find relevant information.
Checking filter logic and retrieval scope helps improve accuracy.
Incorrect data source connection
If the retriever is not properly linked to the PDF storage location, it may fail to retrieve relevant information.
Ensuring a stable connection between Salesforce Data Cloud and the retriever prevents retrieval failures.
Parsing Issues with PDF Files
If PDFs are not properly indexed, AI may struggle to extract relevant content.
Ensuring structured document formatting improves AI comprehension.


質問 # 294
What is the role of the large language model (LLM) in executing an Agent Action?

正解：B

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 # 295
Universal Containers wants to utilize Agentforce for Sales to help sales reps reach their sales quotas by providing AI-generated plans containing guidance and steps for closing deals. Which feature meets this requirement?

正解：B

解説：
Universal Containers (UC) aims to leverage Agentforce for Sales to assist sales reps with AI- generated plans that provide guidance and steps for closing deals.


質問 # 296
An Agentforce implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce.
The Agentforce Specialist needs to configure the system to use the most accurate and up-to-date information for email generation. Which grounding technique should the Agentforce Specialist use?

正解：B

解説：
For Einstein Sales Emails to generate personalized follow-up emails, it is crucial to ground the email content with the most up-to-date and accurate information. Grounding refers to connecting the AI model with real-time data. The most appropriate technique in this case is Ground with Record Merge Fields. This method ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.
Record Merge Fields ensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.
Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.
Automatic grounding using Draft with Einstein is a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data like Record Merge Fields.


質問 # 297
Universal Containers is using Agentforce for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the Agent to match opportunities.

正解：B


質問 # 298
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