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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Related Certifications: | Salesforce AI Associate (retired) Salesforce AI Specialist (retired) Salesforce Platform App Builder Salesforce Platform Administrator |
| Passing Score: | 72%-73% |
| Available Languages: | Japanese, French, English |
| Exam Duration: | 105 minutes |
| Exam Format: | Scenario-based Questions, Multiple Select, Multiple Choice |
| Real Exam Qty: | 60 multiple-choice questions + up to 5 unscored questions |
| Exam Price: | USD 200 |
| Recommended Training: | Salesforce Trailhead Agentforce Learning Paths Agentforce Specialist Exam Prep |
| Exam Registration: | Certification Exam Guide Salesforce Certification Registration |
| Sample Questions: | Salesforce Agentforce-Specialist Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | None |
| Official Syllabus URL: | https://help.salesforce.com/s/articleView?id=005298924&type=1 |
>> Agentforce-Specialist日本語版受験参考書 <<
ショートカットを選択し、テクニックを使用するのはより良く成功できるからです。Agentforce-Specialist認定試験に一発合格できる保障を得たいなら、JPTestKing のAgentforce-Specialist問題集はあなたにとってユニークな、しかも最良の選択です。これは賞賛の声を禁じえない参考書です。この問題集より優秀な試験参考書を見つけることができません。このAgentforce-Specialist問題集では、あなたが試験の出題範囲をより正確に理解することができ、よりよく試験に関連する知識を習得することができます。そして、もし試験の準備をするが足りないとしたら、Agentforce-Specialist問題集に出る問題と回答を全部覚えたらいいです。この問題集には実際のAgentforce-Specialist試験問題のすべてが含まれていますから、それだけでも試験に受かることができます。
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質問 # 31
What is automatically created when a custom search index is created in Data Cloud?
正解：B
解説：
In Salesforce Data Cloud, a custom search index is created to enable efficient retrieval of data (e.g., documents, records) for AI-driven processes, such as grounding Agentforce responses. Let's evaluate the options based on Data Cloud's functionality.
* Option A: A retriever that shares the name of the custom search index.When a custom search index is created in Data Cloud, a corresponding retriever is automatically generated with the same name as the index. This retriever leverages the index to perform contextual searches (e.g., vector-based lookups) and fetch relevant data for AI applications, such as Agentforce prompt templates. The retriever is tied to the indexed data and is ready to use without additional configuration, aligning with Data Cloud's streamlined approach to AI integration. This is explicitly documented in Salesforce resources and is the correct answer.
* Option B: A dynamic retriever to allow runtime selection of retriever parameters without manual configuration.While dynamic behavior sounds appealing, there's no concept of a "dynamic retriever" in Data Cloud that adjusts parameters at runtime without configuration. Retrievers are tied to specific indexes and operate based on predefined settings established during index creation. This option is not supported by official documentation and is incorrect.
* Option C: A predefined Apex retriever class that can be edited by a developer to meet specific needs.Data Cloud does not generate Apex classes for retrievers. Retrievers are managed within the Data Cloud platform as part of its native AI retrieval system, not as customizable Apex code. While developers can extend functionality via Apex for other purposes, this is not an automatic outcome of creating a search index, making this option incorrect.
Why Option A is Correct:
The automatic creation of a retriever named after the custom search index is a core feature of Data Cloud's search and retrieval system. It ensures seamless integration with AI tools like Agentforce by providing a ready-to-use mechanism for data retrieval, as confirmed in official documentation.
References:
Salesforce Data Cloud Documentation: Custom Search Indexes - States that a retriever is auto-created with the same name as the index.
Trailhead: Data Cloud for Agentforce - Explains retriever creation in the context of search indexes.
Salesforce Help: Set Up Search Indexes in Data Cloud - Confirms the retriever-index relationship.
質問 # 32
Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management.
For security purposes,
UC wants the agent to verify a customer's identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed.
Which approach best meets this requirement?
正解：C
解説：
The AgentForce for Service Implementation Guide clearly outlines that when an agent must verify identity before performing any case-related operations, the correct method is to use a variable-based control flow.
The documentation specifies: "To maintain deterministic and secure behavior, define a variable (for example,
'isVerified') that stores the result of an identity verification step. Use this variable as a conditional filter in the topic flow to ensure that case-related actions execute only when the variable equals 'true'." This ensures that no sensitive or case-specific data is shared unless verification is explicitly confirmed. It provides a deterministic safeguard, as the system only proceeds with case data actions after the verification variable confirms completion.
Option A ("Use keywords such as 'Always' and 'Never'") relies on natural language instructions, which are not deterministic and can be misinterpreted by the model. Option C ("Use a global instruction to check the variable") adds unnecessary complexity and lacks the control-level filtering that ensures secure flow logic.
Therefore, Option B correctly implements Salesforce's best-practice pattern for conditional execution using variables and filters in AgentForce.
References (AgentForce Documents / Study Guide):
* AgentForce for Service Configuration Guide: "Identity Verification and Conditional Case Access"
* AgentForce Implementation Handbook: "Using Variables and Filters for Deterministic Agent Actions"
* AgentForce Study Guide: "Secure Flow Design in Service Agents"
質問 # 33
When creating a custom retriever in Einstein Studio, which step is considered essential?
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation:In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating acustom retrieverinvolves setting up a mechanism to fetch data for AI prompts or responses. Theessential stepis defining the foundation of the retriever: selecting thesearch index, specifying thedata model object (DMO), and identifying thedata space(Option A). These elements establish where and what the retriever searches:
* Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
* Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
* Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
* Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever's output, but it's a secondary step. The retriever must first know where to search (A) before output can be configured.
* Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
* Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
References:
* Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)
* Trailhead: "Einstein Studio for Agentforce" (https://trailhead.salesforce.com/content/learn/modules
/einstein-studio-for-agentforce)
質問 # 34
Universal Containers (UC) wants to use Generative AI Salesforce functionality to reduce Service Agent handling time by providing recommended replies based on the existing Knowledge articles. On which AI capability should UC train the service agents?
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation:Salesforce Agentforce leverages generative AI to enhance service agent efficiency, particularly through capabilities that generate recommended replies. In this scenario, Universal Containers aims to reduce handling time by providing replies based on existing Knowledge articles
, which are a core component of Salesforce Knowledge. The Knowledge Replies capability is specifically designed for this purpose-it uses generative AI to analyze Knowledge articles, match them to the context of a customer inquiry (e.g., a case or chat), and suggest relevant, pre-formulated responses for service agents to use or adapt. This aligns directly with UC's goal of leveraging existing content to streamline agent workflows.
* Option A (Service Replies): While "Service Replies" might sound plausible, it is not a specific, documented capability in Agentforce. It appears to be a generic distractor and does not tie directly to Knowledge articles.
* Option B (Case Replies): "Case Replies" is not a recognized AI capability in Agentforce either. While replies can be generated for cases, the focus here is on Knowledge article integration, which points to Knowledge Replies.
* Option C (Knowledge Replies): This is the correct capability, as it explicitly connects generative AI with Knowledge articles to produce recommended replies, reducing agent effort and handling time.
Training service agents on Knowledge Replies ensures they can effectively use AI-suggested responses, review them for accuracy, and integrate them into their workflows, fulfilling UC's objective.
References:
* Salesforce Agentforce Documentation: "Knowledge Replies for Service Agents" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm&type=5)
* Trailhead: "Agentforce for Service" module (https://trailhead.salesforce.com/content/learn/modules
/agentforce-for-service)
質問 # 35
What considerations should an Agentforce Specialist be aware of when using Record Snapshots grounding in a prompt template?
正解：A
解説：
Record Snapshots grounding in Agentforce prompt templates allows the AI to access and use data from a specific Salesforce record (e.g., fields and related records) to generate contextually relevant responses.
However, there are specific limitations to consider. Let's analyze each option based on official documentation.
* Option A: Activities such as tasks and events are excluded.According to Salesforce Agentforce documentation, when grounding a prompt template with Record Snapshots, the data included is limited to the record's fields and certain related objects accessible via Data Cloud or direct Salesforce relationships. Activities (tasks and events) are not included in the snapshot because they are stored in a separate Activity object hierarchy and are not directly part of the primary record's data structure. This is a key consideration for an Agentforce Specialist, as it means the AI won't have visibility into task or event details unless explicitly provided through other grounding methods (e.g., custom queries). This limitation is accurate and critical to understand.
* Option B: Empty data, such as fields without values or sections without limits, is filtered out.
Record Snapshots include all accessible fields on the record, regardless of whether they contain values.
Salesforce documentation does not indicate that empty fields are automatically filtered out when grounding a prompt template. The Atlas Reasoning Engine processes the full snapshot, and empty fields are simply treated as having no data rather than being excluded. The phrase "sections without limits" is unclear but likely a typo or misinterpretation; it doesn't align with any known Agentforce behavior.
This option is incorrect.
* Option C: Email addresses associated with the object are excluded.There's no specific exclusion of email addresses in Record Snapshots grounding. If an email field (e.g., Contact.Email or a custom email field) is part of the record and accessible to the running user, it is included in the snapshot. Salesforce documentation does not list email addresses as a restricted data type in this context, making this option incorrect.
Why Option A is Correct:
The exclusion of activities (tasks and events) is a documented limitation of Record Snapshots grounding in Agentforce. This ensures specialists design prompts with awareness that activity-related context must be sourced differently (e.g., via Data Cloud or custom logic) if needed. Options B and C do not reflect actual Agentforce behavior per official sources.
References:
Salesforce Agentforce Documentation: Prompt Templates > Grounding with Record Snapshots - Notes that activities are not included in snapshots.
Trailhead: Ground Your Agentforce Prompts - Clarifies scope of Record Snapshots data inclusion.
Salesforce Help: Agentforce Limitations - Details exclusions like activities in grounding mechanisms.
質問 # 36
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