さらに、JPNTest AI-201ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1rIAV4EH7KmwLRTxeQYBv7c_tA-M_JvpS
当社は、AI-201トレーニング質問の研究分野で非常に専門的であると信じてください。これは、試験の合格率が高いことで説明できます。他の分野では優れているにもかかわらず、品質と効率がAI-201の実際の試験の最初のものであると常に信じていました。学習資料の場合、合格率は品質と効率の最良のテストです。教材を使用すると、試験に参加できるのは準備に約20〜30時間かかる場合のみです。残りの時間は、やりたいことを何でもできます。これにより、レビューのプレッシャーを完全に軽減できます。 AI-201学習教材の一貫した目的は、時間の節約と効率の向上です。
| Section | Weight | Objectives |
|---|---|---|
| Einstein Trust Layer & Security | - Data Masking
| |
| Agent Force Concepts | 30% | - Einstein GPT Integration
|
| Prompt Engineering | 30% | - Prompt Builder & Template Creation
|
| Reasoning Engine & AI Actions | - Workflow mastery
|
多くの人々は高い難度のSalesforce認証AI-201試験に合格するのは専門の知識が必要だと思います。それは確かにそうですが、その知識を身につけることは難しくないとといわれています。Salesforce業界ではさらに強くなるために強い専門知識が必要です。
質問 # 290
When using a prompt template, what should an Agentforce Specialist consider with their grounding data and chosen model?
正解：A
解説：
The most critical technical consideration when pairing a prompt template's grounding data with a chosen Large Language Model (LLM) is the relationship between the two. The correct action is to review the model limitation in Prompt Builder versus the grounding data size (C).
Every LLM has a fixed context window limit, typically expressed in tokens (the model's units for processing text). This token limit defines the maximum amount of input data (the prompt template text + all the dynamic grounding data) and output data the model can handle in a single request.
The grounding data, which is pulled dynamically from Salesforce records (e.g., related lists, long text fields, Flow outputs), varies significantly in size from one record to the next. If the combined size of the prompt and the dynamic data for a specific record exceeds the LLM's token limit, the generative AI request will fail with a "token limit exceeded" error. The Agentforce Specialist must proactively design the template to limit the amount of data retrieved (e.g., using Flow to summarize related lists or querying only essential fields) to ensure it stays within the chosen model's capacity.
質問 # 291
Universal Containers (UC) wants to enable its sales reps to explore opportunities that are similar to previously won opportunities by entering the utterance, "Show me other opportunities like this one." How should UC achieve this with Agents?
正解：A
解説：
Universal Containers can achieve the request to explore similar opportunities by using the standard Copilot action. Agent has built-in actions to handle natural language queries, such as
"Show me other opportunities like this one." The standard action will process the query and return results based on predefined matching criteria like opportunity details and past Closed Won deals.
This approach avoids the need to create custom flows or Apex classes, leveraging out-of-the-box functionality.
質問 # 292
Universal Containers (UC) has a legacy system that needs to integrate with Salesforce. UC wishes to create a digest of account action plans using the generative API feature. Which API service should UC use to meet this requirement?
正解：B
解説：
To create a digest of account action plans using the generative API feature, Universal Containers should use the REST API. The REST API is ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.
Metadata API is used for retrieving and deploying metadata, not for data operations like generating summaries.
SOAP API is an older API used for integration but is less flexible compared to REST for this specific use case.
質問 # 293
Universal Containers (UC) wants to offer personalized service experiences and reduce agent handling time with Al-generated email responses, grounded in Knowledge base. Which AI capability should UC use?
正解：B
解説：
For Universal Containers (UC) to offer personalized service experiences and reduce agent handling time using AI-generated responses grounded in the Knowledge base, the best solution is Einstein Service Replies for Email. This capability leverages AI to automatically generate responses to service- related emails based on historical data and the Knowledge base, ensuring accuracy and relevance while saving time for service agents.
質問 # 294
A Salesforce Agentforce Specialist is reviewing the feedback from a customer about the ineffectiveness of the prompt template. What should the Agentforce Specialist do to ensure the prompt template's effectiveness?
正解：B
解説：
To address the ineffectiveness of a prompt template reported by a customer, the Salesforce Agentforce Specialist should use the Prompt Builder Scorecard (Option B). This tool is explicitly designed to evaluate and monitor prompt templates against key criteria such as relevance, accuracy, safety, and grounding. By leveraging the scorecard, the specialist can systematically identify weaknesses in the template and make data-driven refinements. While monitoring and refining based on user feedback (Option A) is a general best practice, the Prompt Builder Scorecard is Salesforce's recommended tool for structured evaluation, aligning with documented processes for maintaining prompt effectiveness. Changing the grounding object (Option C) without proper evaluation is reactive and does not address the root cause.
質問 # 295
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JPNTestの商品はSalesforce業界の専門家が自分の豊かな知識と経験を利用して認証試験に対して研究出たので品質がいいのAI-201試験の資料でございます。受験者がJPNTestを選択したら高度専門のAI-201試験に１００％合格することが問題にならないと保証いたします。
AI-201対応受験: https://www.jpntest.com/shiken/AI-201-mondaishu
P.S. JPNTest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=1rIAV4EH7KmwLRTxeQYBv7c_tA-M_JvpS