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私たちが知っているように、一部の人々は以前に試験に失敗し、Agentforce-Specialistトレーニング資料を購入する前にこの苦しい試験に自信を失いました。私たちはここで悲しみを分けます。これから時間のかかる思考を捨てることができます。対照的に、それらは不明瞭なコンテンツを感じることなくあなたの可能性を刺激します。 Agentforce-Specialist試験準備を取得した後、試験期間中に大きなストレスにさらされることはありません。
| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Related Certifications: | Salesforce Platform App Builder Salesforce AI Specialist (retired) Salesforce Platform Administrator Salesforce AI Associate (retired) |
| Exam Duration: | 105 minutes |
| Real Exam Qty: | 60 multiple-choice questions + up to 5 unscored questions |
| Exam Format: | Multiple Choice, Scenario-based Questions, Multiple Select |
| Available Languages: | Japanese, English, French |
| Passing Score: | 72%-73% |
| Exam Price: | USD 200 |
| Recommended Training: | Salesforce Trailhead Agentforce Learning Paths Agentforce Specialist Exam Prep |
| Exam Registration: | Salesforce Certification Registration Certification Exam Guide |
| 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復習時間 <<
Fast2testのSalesforceのAgentforce-Specialist試験トレーニング資料は豊富な経験を持っているIT専門家が研究したもので、問題と解答が緊密に結んでいるものです。SalesforceのAgentforce-Specialist試験トレーニング資料は現在、市場上で一番質のいい学習教材です。Fast2testは無料でサンプルを提供することができる。うちの学習教材は君の認定試験に合格することに大変役に立ちます。
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質問 # 129
Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein's generative AI service.
No data is used for LLM training or product improvements by third- party LLMs.
No data is retained outside of UC's Salesforce org.
The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should the Agentforce Specialist highlight to UC that addresses these requirements?
正解：A
解説：
Universal Containers (UC)has concerns about data privacy when usingSalesforce's generative AIfeatures, particularly around preventing third-party LLMs from accessing or retaining their data. TheZero-Data Retention Policyin theEinstein Trust Layeris designed to address these concerns by ensuring that:
* No data is used for trainingor product improvements by third-party LLMs.
* No data is retainedoutside of the customer's Salesforce organization.
* The LLM provider cannot access any customer data.
This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging generative AI capabilities.
* Prompt DefenseandData Maskingare also security features, but they do not directly address the concerns related to third-party data access and retention.
:
Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer.htm
質問 # 130
Universal Containers built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors. What is the cause of the random nature of this error?
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM's token capacity (e.g., input + output tokens). The random nature of these failures suggests variability in the token count across different records, which is directly addressed by Option B.
Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record-some records might have short text fields while others have lengthy ones-the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM's limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.
* Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation does not define "Flex" as a specific template type for handling token variability in this context (there are Flow-based templates, but they're unrelated to token limits). This option is a distractor and not a verified solution.
* Option C: The LLM's token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens for advanced models) and does not vary with user demand. Demand might affect performance or availability, but not the token limit itself.
Option B is the correct answer because it accurately identifies the dynamic nature of the prompt template as the root cause of variable token counts leading to random failures.
References:
* Salesforce Agentforce Documentation: "Prompt Templates" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
* Trailhead: "Build Prompt Templates for Agentforce" (https://trailhead.salesforce.com/content/learn
/modules/build-prompt-templates-for-agentforce)
質問 # 131
An Agentforce Specialist is tasked with analyzing Agent interactions, looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the Agentforce Specialist to achieve this?
正解：C
解説：
The task requires analyzing user inputs, requests, and queries to identify patterns and trends in Agentforce interactions. Let's assess the options based on Agentforce's analytics capabilities.
Option A: Agent Event Logs dashboard.Agent Event Logs capture detailed technical events (e.g., API calls, errors, or system-level actions) related to agent operations. While useful for troubleshooting or monitoring system performance, they are not designed to analyze user inputs or conversational trends. This option does not meet the requirement and is incorrect.
Option B: AI Audit and Feedback Data dashboard.There's no specific "AI Audit and Feedback Data dashboard" in Agentforce documentation. Feedback mechanisms exist (e.g., user feedback on responses), and audit trails may track changes, but no single dashboard combines these for analyzing user queries and trends.
This option appears to be a misnomer and is incorrect.
Option C: User Utterances dashboard.The User Utterances dashboard in Agentforce Analytics is specifically designed to analyze user inputs, requests, and queries. It aggregates and visualizes what users are asking the agent, identifying patterns (e.g., common topics) and trends (e.g., rising query types). Specialists can use this to refine agent instructions or topics, making it the perfect tool for this task. This is the correct answer per Salesforce documentation.
Why Option C is Correct:
The User Utterances dashboard is tailored for conversational analysis, offering insights into user interactions that align with the specialist's goal of identifying patterns and trends. It's a documented feature of Agentforce Analytics for post-deployment optimization.
References:
Salesforce Agentforce Documentation: Agent Analytics > User Utterances Dashboard - Describes its use for analyzing user queries.
Trailhead: Monitor and Optimize Agentforce Agents - Highlights the dashboard's role in trend identification.
Salesforce Help: Agentforce Dashboards - Confirms User Utterances as a key tool for interaction analysis.
質問 # 132
An Agentforce at Universal Containers is trying to set up a new Field Generation prompt template. They take the following steps.
1. Create a new Field Generation prompt template.
2. Choose Case as the object type.
3. Select the custom field AI_Analysis_c as the target field.
After creating the prompt template, the Agentforce Specialist saves, tests, and activates it. Howsoever, when they go to a case record, the AI Analysis field does not show the (Sparkle) icon on the Edit pencil. When the Agentforce Specialist was editing the field, it was behaving as a normal field.
Which critical step did the Agentforce Specialist miss?
正解：A
解説：
For Field Generation prompt templates to display the Sparkle icon (indicating AI-generated content), the target field must be explicitly associated with the prompt template on the Lightning page layout. Even if the prompt template is activated, failing to add the field to the page layout and link it to the template will result in the field behaving as a standard field. Salesforce documentation emphasizes that page layout configuration is mandatory to enable AI-driven field interactions.
Reactivating the layout (A) is unnecessary unless the layout itself was modified after activation.
Case objects are supported for Field Generation (B is incorrect).
Reference:
Salesforce Help Article: Configure Field Generation Prompt Templates ("Associating Fields with Page Layouts" section).
Einstein GPT Implementation Guide: "Enabling AI-Generated Fields in Lightning Pages."
質問 # 133
What is one key purpose of action instructions when creating a custom agent action in Agentforce?
正解：C
解説：
According to the AgentForce Action Design and Configuration Guide, action instructions serve as the directive text that informs the Reasoning Engine about when and how to invoke a specific action. The documentation states: "Action instructions guide the reasoning engine by describing the action's purpose, inputs, and when it should be selected. Clear, concise instructions improve the LLM's accuracy in mapping user intent to the correct action." Option A is correct because it reflects this documented purpose - connecting user intent with the appropriate system action.
Option B is incorrect since the temperature parameter for the LLM is defined at the system or configuration level, not within action instructions.
Option C is incorrect because action instructions are meant for the reasoning engine, not for end users.
References (AgentForce Documents / Study Guide):
* AgentForce Action Design Guide: "How Action Instructions Support Reasoning"
* AgentForce Builder Handbook: "Optimizing Action Selection through Clear Instructions"
* AgentForce Study Guide: "Purpose of Action Instructions in Custom Actions"
質問 # 134
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