最上級のAI-201試験問題Salesforceテスト最高成績で最速合格をゲットせよ！
試験準備には最適なAI-201試験問題2026年最新のAI Specialist究極な315問があります
質問 # 14
Universal Containers wants an AI agent to answer questions about warranties using unstructured data stored in Data Cloud. Results must be filterable by product line and ranked by recent updates.
- A. Apply semantic embeddings with default metadata filters to achieve the desired result.
- B. Build a custom retriever in Einstein Studio with product line filters and recency ranking.
- C. Use the default retriever which automatically accounts for recency ranking.
正解：B
質問 # 15
How does Agentforce select the correct action to resolve a user's request?
- A. Each topic contains a list of the matching action's user utterances so that the agent can map the user request to the right topic and action.
- B. The large language model (LLM) selects the right topic and action, if they exist. If there are no matches, the LLM attempts to answer the user's request.
- C. The reasoning engine identifies the agent action to be executed by its name and action input instructions.
正解：B
解説：
In the AgentForce Architecture and Reasoning Engine Overview, Salesforce explains that the large language model (LLM) drives topic and action selection. The documentation states:
"AgentForce uses an LLM to interpret user intent, map it to existing topics, and trigger the appropriate action when available. If no matching topic or action is found, the LLM attempts to generate a direct response using its available context."
質問 # 16
Universal Containers wants to implement a customer verification process where sensitive account information can only be accessed after the customer passes identity verification. The agent must enforce this security rule deterministically without allowing the large language model (LLM) to bypass the verification requirement. What should an Agentforce Specialist recommend as the best solution?
- A. Use context variables to store verification status in the messaging session and configure the agent to check these variables through natural language prompts during each sensitive action.
- B. Include detailed verification instructions in the agent's topic instructions explaining when customers should be verified and rely on the LLM to follow these guidelines consistently across all interactions.
- C. Create a custom variable IsCustomerVerified set by a verification action, then apply a conditional filter using the expression IsCustomerVerified equals true to all sensitive data actions, ensuring deterministic access control that the LLM can't alter.
正解：C
解説：
The AgentForce Security and Deterministic Logic Guide specifies that sensitive actions must be gated through conditional filters linked to verification variables, not through natural language. It states: "For any process requiring secure, deterministic access, create a custom variable (e.g., IsCustomerVerified) that stores the verification status as a Boolean. Apply a filter expression to all protected actions (e.g., IsCustomerVerified = true). This ensures the LLM cannot bypass or alter access logic." This configuration ensures security and determinism because the execution of sensitive actions is programmatically enforced, not dependent on the LLM's understanding.
質問 # 17
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?
- A. Ground with Record Merge Fields
- B. Ground with Apex Merge Fields
- C. Automatic grounding using Draft with Einstein feature
正解：C
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 # 18
Universal Containers, dealing with a high volume of chat inquiries, implements Einstein Work Summaries to boost productivity. After an agent-customer conversation, which additional information does Einstein generate and fill, apart from the "summary"'
- A. Sentiment Analysis and Emotion Detection
- B. Issue and Revolution
- C. Draft Survey Request Email
正解：B
解説：
Einstein Work Summaries automatically generate concise summaries of customer interactions (e.g., chat transcripts). Beyond the "summary" field, it extracts and populates Issue (key problem discussed) and Resolution (action taken to resolve the issue). These fields help agents and upervisors quickly grasp the conversation's context without reviewing the full transcript.
質問 # 19
When a customer chat is initiated, which functionality in Salesforce provides generative AI replies or draft emails based on recommended Knowledge articles?
- A. Einstein Grounding
- B. Einstein Service Replies
- C. Einstein Reply Recommendations
正解：B
解説：
When a customer chat is initiated, Einstein Service Replies provides generative AI replies or draft emails based on recommended Knowledge articles. This feature uses the information from the Salesforce Knowledge base to generate responses that are relevant to the customer's query, improving the efficiency and accuracy of customer support interactions.
質問 # 20
Universal Containers wants to reduce overall customer support handling time by minimizing the time spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields. Which combination of Agentforce for Service features enables this effort?
- A. Einstein Service Replies and Work Summaries
- B. Einstein Reply Recommendations and Case Summaries
- C. Einstein Reply Recommendations and Case Classification
正解：B
解説：
Universal Containers (UC) aims to streamline customer support by addressing two goals:
reducing in-chat typing time for routine answers and minimizing post-chat analysis by auto- suggesting case field values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification (Option A) are the ideal combination to achieve this.
Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to- use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC's first goal.
Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC's second goal.
質問 # 21
A Salesforce Administrator wants to generate personalized, targeted emails that incorporate customer interaction data. The admin wants to leverage large language models (LLMs) to write the emails, and wants to reuse templates for different products and customers. Which solution approach should the admin leverage?
- A. Create a Sales Email prompt template type.
- B. Use sales Email standard templates
- C. Create a t field Generation prompt template type
正解：A
解説：
To generate personalized emails using LLMs while reusing templates:
Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.
質問 # 22
What is An Agentforce able to do when the "Enrich event logs with conversation data" setting in Agent is enabled?
- A. Generate details reports on all Copilot conversations over any time period.
- B. View the user click path that led to each copilot action.
- C. View session data including user Input and copilot responses for sessions over the past 7 days.
正解：C
解説：
When the "Enrich event logs with conversation data" setting is enabled in Agent, it allows An Agentforce or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.
This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement.
質問 # 23
An AgentForce Specialist wants to troubleshoot an agent that is hallucinating weblinks. The agent has an action that uses a prompt template, which is using a knowledge retriever, to generate the output text that the agent will use. Which process is appropriate to find the root cause of the hallucination behavior?
- A. Examine the prompt instructions and contents of the chunks shown in the resolved prompt output.
- B. Examine the topic name and classification description for hallucination guardrails.
- C. Examine the topic instructions and ensure the word "ALWAYS" is used in the hallucination guardrails.
正解：A
解説：
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Troubleshooting and Optimization Guide, hallucinations - instances where the agent fabricates details such as weblinks or data - often occur due to issues in prompt construction or retrieved content grounding. The recommended diagnostic process involves inspecting the prompt template instructions and reviewing the resolved prompt output, including the actual retrieved knowledge chunks.
By examining these areas, the AgentForce Specialist can determine whether the hallucinated content originates from ambiguous prompt phrasing, missing grounding variables, or irrelevant retrieval results. This approach ensures an evidence-based investigation directly linked to the agent's reasoning and generation steps.
質問 # 24
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?
- A. Use Flow and External Services to bring data from an external LLM.
- B. Use BYO-LLM functionality in Einstein Studio.
- C. Use Apex to connect to an external LLM and ground the prompt.
正解：B
解説：
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem. Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
質問 # 25
A Universal Containers administrator is setting up Einstein Data Libraries. After creating a new library, the administrator notices that only the file upload option is available; there is no option to configure the library using a Salesforce Knowledge base. What is the most likely cause of this Issue?
- A. The current Salesforce org lacks the necessary Einstein for Service permissions that support the Knowledge-based Data Library option, so only the file upload option is presented.
- B. The administrator is not using Lightning Experience, which is required to display all data source options, Including the Knowledge base option, when configuring Einstein Data Libraries.
- C. Salesforce Knowledge is not enabled in the organization; without Salesforce Knowledge enabled, the Knowledge-based data source option will not be available in Einstein Data Libraries.
正解：C
解説：
Why is "Salesforce Knowledge is not enabled" the correct answer?
If an administrator only sees the file upload option in Einstein Data Libraries and cannot configure a Salesforce Knowledge base, the most likely reason is that Salesforce Knowledge is not enabled in the organization.
Key Considerations for Einstein Data Libraries:
Salesforce Knowledge Integration is Optional
Einstein Data Libraries can pull knowledge data only if Salesforce Knowledge is enabled.
If Knowledge is not activated, the system will default to file uploads as the only available option.
How to Fix This Issue?
The administrator should enable Salesforce Knowledge in Setup Knowledge Settings.
Once enabled, the option to configure Knowledge-based Data Libraries will become available.
質問 # 26
How does Secure Data Retrieval ensure that only authorized users can access necessary Salesforce data for dynamic grounding?
- A. Retrieves Salesforce data based on the 'Run As" users permissions.
- B. Retrieves Salesforce data based on the user's permissions executing the prompt.
- C. Retrieves Salesforces data based on the Prompt template's object permissions.
正解：B
解説：
Secure Data Retrieval enforces Salesforce's security model by dynamically grounding data access in the permissions of the user executing the prompt. This ensures compliance with CRUD (Create, Read, Update, Delete) and FLS (Field-Level Security) settings, preventing unauthorized access to sensitive data. For example, if a user lacks access to a specific object or field, the AI model cannot retrieve it for dynamic grounding.
質問 # 27
An agent incorrectly updates records when given ambiguous user instructions. What is the BEST mitigation strategy?
- A. Remove update permissions
- B. Increase the number of skills
- C. Disable automation
- D. Improve prompt instructions and constraints
正解：D
解説：
Clear prompt instructions, guardrails, and constraints reduce unintended actions caused by ambiguity.
質問 # 28
Universal Containers has multiple Salesforce orgs, each with a unique customer service agent where a verification agent must pass customer identity data to downstream agents handling account modifications. The customer ID must remain secure and persistent across agent handoffs without exposure to large language model (LLM) modification. What is the most appropriate configuration?
- A. Implement a custom object to temporarily store verification status and have each agent query it via SOQL actions during execution.
- B. Store customer identity information in conversation variables created by the first agent and have other agents read those same conversation variables.
- C. Use the Agent API to start the downstream agent's session and pass the verified customer ID as a read-only context variable, ensuring security and preventing LLM alteration.
正解：C
解説：
The AgentForce Inter-Agent Communication and Security Configuration Guide specifies that when sensitive identity data (like a verified customer ID) must be shared between agents, the correct approach is to use the Agent API to initiate the downstream agent's session. The verified data should be passed as a read-only context variable, ensuring persistence across sessions while preventing modification by the large language model (LLM).
This setup maintains data integrity and security compliance by isolating sensitive variables from the LLM's reasoning layer. Context variables passed via the Agent API are immutable during runtime, ensuring they cannot be altered or exposed in agent-generated responses.
質問 # 29
Universal Containers wants to incorporate the current order fulfillment status into a prompt for a large language model (LLM). The order status is stored in the external enterprise resource planning (ERP) system. Which data grounding technique should the Agentforce Specialist recommend?
- A. Apex Merge Fields
- B. External Services Merge Fields
- C. Eternal Object Record Merge Fields
正解：C
解説：
Context of the Requirement:
Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.
Data Grounding in LLM Prompts:
Data grounding ensures the Large Language Model has access to the most current and relevant information. In Salesforce, one recommended approach is to use External Objects (via Salesforce Connect) when data resides outside of Salesforce.
質問 # 30
Universal Containers has created an Employee Agent. Which step should an Agentforce Specialist take to connect the agent with a Slack channel?
- A. Create a connection between Salesforce and the Slack workspace.
- B. Create an embedded service deployment and connection between Salesforce and the Slack workspace.
- C. Create an Omni-Channel flow and connection between Salesforce and the Slack workspace.
正解：A
解説：
According to the AgentForce for Slack Integration Guide, to connect an Employee Agent (or any internal AgentForce agent) with a Slack channel, the required setup step is to create a connection between Salesforce and the Slack workspace. The documentation specifies: "Before deploying an Employee Agent into Slack, you must establish a secure connection between your Salesforce org and the Slack workspace. This connection enables authentication, permission mapping, and message exchange between the Agent and Slack users." Once the connection is established, the administrator can configure the specific Slack channel where the agent will operate.
質問 # 31
Universal Containers has a custom Agent action calling a flow to retrieve the real-time status of an order from the order fulfillment system. For the given flow, what should the Agentforce Specialist consider about the running user's data access?
- A. The flow must have the "with sharing" permission selected m the advanced settings for the permissions, field-level security, and sharing settings to be respected.
- B. The Agent will always run flows in system mode so the running user's data access will not affect the data returned.
- C. The custom action adheres to the permissions, held-level security, and sharing settings configured in the flow.
正解：C
解説：
When a flow is invoked via a custom Agent action, its data access depends on the flow's runtime configuration, not system mode by default. Salesforce flows can be configured to respect the running user's permissions and sharing settings:
If the flow is set to "Run as the User Who Launched the Flow" (enabled in Flow Settings), it adheres to the user's permissions, field-level security (FLS), and sharing rules.
質問 # 32
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