Salesforce Agentforce-Specialist 덤프의 높은 적중율에 놀란 회원분들이 계십니다. 고객님들의 도와 Salesforce Agentforce-Specialist 시험을 쉽게 패스하는게 저희의 취지이자 최선을 다해 더욱 높은 적중율을 자랑할수 있다록 노력하고 있습니다. 뿐만 아니라 Fast2test에서는한국어 온라인서비스상담, 구매후 일년무료업데이트서비스, 불합격받을수 환불혹은 덤프교환 등탄탄한 구매후 서비스를 제공해드립니다.
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>> Agentforce-Specialist시험대비 공부문제 <<
Fast2test의Salesforce인증 Agentforce-Specialist덤프는 인터넷에서 검색되는Salesforce인증 Agentforce-Specialist시험공부자료중 가장 출중한 시험준비 자료입니다. Salesforce인증 Agentforce-Specialist덤프를 공부하면 시험패스는 물론이고 IT지식을 더 많이 쌓을수 있어 일거량득입니다.자격증을 취득하여 자신있게 승진하여 연봉협상하세요.
질문 # 185
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?
정답：A
설명：
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)
질문 # 186
Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should An Agentforce recommend to resolve the issue?
정답：A
설명：
When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the Agentforce Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
* Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
* Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce Agentforce Specialist References:For more information, refer to: https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer_data_masking.htm
질문 # 187
A service agent is looking at a custom object that stores travel information. They recently received a weather alert and now need to cancel flights for the customers that are related with this itinerary. The service agent needs to review the Knowledge articles about canceling and rebooking the customer flights.
Which Agent capability helps the agent accomplish this?
정답：C
설명：
In this scenario, the Agent capability that best helps the agent is its ability to execute tasks based on available actions and answer questions using data from Knowledge articles. Agent can assist the service agent by providing relevant Knowledge articles on canceling and rebooking flights, ensuring that the agent has access to the correct steps and procedures directly within the workflow.
This feature leverages the agent's existing context (the travel itinerary) and provides actionable insights or next steps from the relevant Knowledge articles to help the agent quickly resolve the customer's needs.
The other options are incorrect:
* B refers to invoking a flow to create a Knowledge article, which is unrelated to the task of retrieving existing Knowledge articles.
* C focuses on generating Knowledge articles, which is not the immediate need for this situation where the agent requires guidance on existing procedures.
References:
* Salesforce Documentation on Agent
* Trailhead Module on Einstein for Service
질문 # 188
Once a data source is chosen for an Agentforce Data Library, what is true about changing that data source later?
정답：B
설명：
Why is "The data source cannot be changed after it is selected" the correct answer?
When configuring an Agentforce Data Library, the data source selection is permanent. Once a data source is set, it cannot be modified or replaced. This design ensures data consistency, security, and reliability within Salesforce's AI-driven environment.
Key Considerations in Agentforce Data Library
* Data Source Lock-In
* The chosen data source remains fixed to maintain data integrity and avoid inconsistencies.
* Any updates or modifications require creating a new Data Library instead of modifying the existing one.
* Why Can't the Data Source Be Changed?
* The data source defines the foundation of AI-driven workflows, and any modification would disrupt processing logic.
* Agentforce tools rely on structured datasets to enable AI-powered recommendations, and changing data sources could lead to inconsistencies in grounding techniques.
* Workarounds for Changing Data Sources
* If an organization needs to use a different data source, a new Agentforce Data Library must be created and configured from scratch.
* Old data can be manually migrated into the new data source for continuity.
Why Not the Other Options?
# A. The data source can be changed through the Data Cloud settings.
* Incorrect because once the data source is linked to an Agentforce Data Library, it cannot be altered, even via Data Cloud settings.
# B. The Data Retriever can be reconfigured to use a different data source.
* Incorrect as the Data Retriever works within the constraints of the selected data source and does not provide an option to swap data sources post-selection.
Agentforce Specialist References
The Salesforce AI Specialist Material and Salesforce Instructions for the Certification confirm that once a data source is set for an Agentforce Data Library, it cannot be changed.
질문 # 189
Universal Containers (UC) is discussing its AI strategy in an agile Scrum meeting.
Which business requirement would lead An Agentforce to recommend connecting to an external foundational model via Einstein Studio (Model Builder)?
정답：A
설명：
Einstein Studio (Model Builder) allows organizations to connect and utilize external foundational models while fine-tuning them with company-specific data. This capability is particularly suited to businesses like Universal Containers (UC) that require customization of foundational models to better align with their unique data and use cases.
* Option A: Adjusting model temperature is a parameter-level setting for controlling randomness in AI- generated responses but does not necessitate connecting to an external foundational model.
* Option B: This is the correct answer because Einstein Studio supports fine-tuning external models with proprietary company data, enabling a tailored and more accurate AI solution for UC.
* Option C: Changing frequency penalties is another parameter-level adjustment and does not require external foundational models or Einstein Studio.
질문 # 190
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Fast2test는 여러분이Salesforce 인증Agentforce-Specialist인증시험 패스와 추후사업에 모두 도움이 되겠습니다. Fast2test제품을 선택함으로 여러분은 시간도 절약하고 돈도 절약하는 일석이조의 득을 얻을수 있습니다. 또한 구매후 일년무료 업데이트 버전을 받을수 있는 기회를 얻을수 있습니다. Salesforce 인증Agentforce-Specialist 인증시험패스는 아주 어렵습니다. 자기에 맞는 현명한 학습자료 선택은 성공의 지름길을 내딛는 첫발입니다. 퍼펙트한 자료만이 시험에서 성공할수 있습니다. Fast2test시험문제와 답이야 말로 퍼펙트한 자료이죠. Fast2test Salesforce 인증Agentforce-Specialist인증시험자료는 100% 패스보장을 드립니다.
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