Web-based Agentforce-Specialist Practice Test With Dumps

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Salesforce Agentforce-Specialist Exam Syllabus Topics:

Topic	Details
Topic 1	Agentforce Concepts: This section assesses the skills of AI Engineers and covers how Agentforce works, including its reasoning engine, standard and custom topics, agent actions, and user security management. It also includes testing and deploying agents from sandbox to production environments.
Topic 2	Agentforce and Service Cloud: This section measures the skills of AI Engineers and focuses on building agents that answer questions based on Knowledge articles and connecting them to digital channels. It also covers identifying the correct generative AI features in Agentforce for Service Cloud scenarios.
Topic 3	Agentforce and Data Cloud: This section measures the skills of AI Developers and addresses how Agentforce integrates with Data Cloud to improve response accuracy and personalize answers. It involves grounding with retrievers in Data Cloud to enhance agent performance.
Topic 4	Prompt Engineering: This section measures the skills of AI Developers and focuses on prompt engineering techniques. It covers identifying when to use Prompt Builder, managing prompt templates, selecting appropriate grounding techniques, and explaining the process for creating and executing prompt templates.
Topic 5	Agentforce and Sales Cloud: This section assesses the skills of AI Developers and covers identifying the correct generative AI features in Agentforce for Sales Cloud scenarios. It also includes determining when to use Agentforce Sales Agents, such as Sales Development Representatives (SDRs) and Sales Coaches.

Salesforce Certified Agentforce Specialist Sample Questions (Q203-Q208):

NEW QUESTION # 203
Universal Containers is very concerned about security compliance and wants to understand:
Which prompt text is sent to the large language model (LLM)
* How it is masked
* The masked response
What should theAgentforce Specialistrecommend?

A. Review the debug logs of the running user.
B. Enable audit trail in the Einstein Trust Layer.
C. Ingest the Einstein Shield Event logs into CRM Analytics.

Answer: B

Explanation:
To addresssecurity complianceconcerns and provide visibility into theprompt text sent to the LLM, how it ismasked, and themasked response, theAgentforce Specialistshould recommend enabling theaudit trail in the Einstein Trust Layer. This feature captures and logs the prompts sent to the large language model (LLM) along with the masking of sensitive information and the AI's response. This audit trail ensures full transparency and compliance with security requirements.
* Option A (Einstein Shield Event logs)is focused on system events rather than specific AI prompt data.
* Option B (debug logs)would not provide the necessary insight into AI prompt masking or responses.
For further details, refer toSalesforce's Einstein Trust Layer documentationabout auditing and security measures.

NEW QUESTION # 204
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 Reply Recommendations and Case Summaries
B. Einstein Reply Recommendations and Case Classification
C. Einstein Service Replies and Work Summaries

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation: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 RecommendationsandCase 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.
* Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don't directly reduce post-chat field entry time.
* Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.
* Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post-chat automation (Case Classification).
Thus, Option A is the correct answer for UC's needs.
References:
* Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm&type=5)
* Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help:https://help.salesforce.
com/s/articleView?id=sf.case_classification.htm&type=5)
* Trailhead: "Agentforce for Service" (https://trailhead.salesforce.com/content/learn/modules/agentforce- for-service)

NEW QUESTION # 205
Where should theAgentforce Specialistgo to add/update actions assigned to a copilot?

A. Copilot Actions page or Global Actions
B. Copilot Detail page, Global Actions, or the record page for the copilot action
C. Copilot Actions page, the record page for the copilot action, or the Copilot Action Library tab

Answer: C

Explanation:
To add or update actions assigned to a copilot, An Agentforce can manage this through several areas:
* Copilot Actions Page: This is the central location where copilot actions are managed and configured.
* Record Page for the Copilot Action: From the record page, individual copilot actions can be updated or modified.
* Copilot Action Library Tab: This tab serves as a repository where predefined or custom actions for Copilot can be accessed and modified.
These areas provide flexibility in managing and updating the actions assigned to Copilot, ensuring that the AI assistant remains aligned with business requirements and processes.
The other options are incorrect:
* Bmisses the Copilot Action Library, which is crucial for managing actions.
* Cincludes the Copilot Detail page, which isn't the primary place for action management.
References:
* Salesforce Documentation onManaging Copilot Actions
* SalesforceAgentforce SpecialistGuide onCopilot Action Management

NEW QUESTION # 206
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?

A. Use Apex to connect to an external LLM and ground the prompt.
B. Use Flow and External Services to bring data from an external LLM.
C. Use BYO-LLM functionality in Einstein Studio.

Answer: C

Explanation:
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.
* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.
* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.
References:
Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm

NEW QUESTION # 207
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?

A. Identify the best matching actions and correct order of execution
B. Find similar requests and provide actions that need to be executed
C. Determine a user's access and sort actions by priority to be executed

Answer: A

Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user' s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models

NEW QUESTION # 208
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WEB-BASED AGENTFORCE-SPECIALIST PRACTICE TEST WITH DUMPS

Web-based Agentforce-Specialist Practice Test With Dumps