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Salesforce Agentforce-Specialist Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
Topic 3	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 4	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 5	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.

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Salesforce Certified Agentforce Specialist Sample Questions (Q79-Q84):

NEW QUESTION # 79
Universal Containers (UC) wants to build an Agentforce Service Agent that provides the latest, active, and relevant policy and compliance information to customers. The agent must:
* Semantically search HR policies, compliance guidelines, and company procedures.
* Ensure responses are grounded on published Knowledge.
* Allow Knowledge updates to be reflected immediately without manual reconfiguration.What should UC do to ensure the agent retrieves the right information?

A. Enable the agent to search all internal records and past customer inquiries.
B. Manually add policy responses into the AI model to prevent hallucinations.
C. Set up an Agentforce Data Library to store and index policy documents for AI retrieval.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC requires an Agentforce Service Agent to deliver accurate, up-to-date policy and compliance info with specific criteria. Let's evaluate.
* Option A: Enable the agent to search all internal records and past customer inquiries.Searching all records and inquiries risks irrelevant or outdated responses, conflicting with the need for published Knowledge grounding and immediate updates. This lacks specificity, making it incorrect.
* Option B: Set up an Agentforce Data Library to store and index policy documents for AI retrieval.The Agentforce Data Library integrates with Salesforce Knowledge, indexing HR policies, compliance guidelines, and procedures for semantic search. It ensures grounding in published Knowledge articles, and updates (e.g., new article versions) are reflected instantly without reconfiguration, as the library syncs with Knowledge automatically. This meets all UC requirements, making it the correct answer.
* Option C: Manually add policy responses into the AI model to prevent hallucinations.Manually embedding responses into the model isn't feasible-Agentforce uses pretrained LLMs, not custom training. It also doesn't support real-time updates, making this incorrect.
Why Option B is Correct:The Data Library meets all criteria-semantic search, Knowledge grounding, and instant updates-per Salesforce's recommended approach.
References:
* Salesforce Agentforce Documentation: Data Library > Knowledge Integration- Details indexing and updates.
* Trailhead: Build Agents with Agentforce- Covers Data Library for accurate responses.
* Salesforce Help: Grounding with Knowledge- Confirms real-time sync.

NEW QUESTION # 80
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 Service Replies and Work Summaries
C. Einstein Reply Recommendations and Case Classification

Answer: C

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 # 81
A sales manager needs to contact leads at scale with hyper-relevant solutions and customized communications in the most efficient manner possible. Which Salesforce solution best suits this need?

A. Einstein Lead follow-up
B. Prompt Builder
C. Einstein Sales Assistant

Answer: B

Explanation:
Step 1: Define the Requirements
The question specifies a sales manager's need to:
* Contact leads at scale: Handle a large volume of leads simultaneously.
* Hyper-relevant solutions: Deliver tailored solutions based on lead-specific data (e.g., CRM data, behavior).
* Customized communications: Personalize outreach (e.g., emails, messages) for each lead.
* Most efficient manner possible: Minimize manual effort and maximize automation.
This suggests a solution that leverages AI for personalization and automation for scale, ideally within the Salesforce ecosystem.
Step 2: Evaluate the Provided Options
A: Einstein Sales Assistant
* Description: Einstein Sales Assistant is not a distinct, standalone product in Salesforce documentation as of March 2025 but is often associated with features in Sales Cloud Einstein or Einstein Copilot for Sales. It typically acts as an AI-powered assistant embedded in the sales workflow, offering suggestions (e.g., next best actions), drafting emails, or summarizing calls.
* Analysis Against Requirements:
* Scale: It supports individual reps by enhancing productivity (e.g., drafting personalized emails quickly), but it doesn't inherently contact leads at scale autonomously. It requires human initiation for each interaction.
* Hyper-relevance: It leverages CRM data to provide relevant suggestions, making it capable of tailoring solutions.
* Customization: It can generate customized communications (e.g., emails grounded in CRM data), but this is manual or semi-automated.
* Efficiency: It streamlines rep tasks but lacks the autonomy to handle large-scale outreach without significant human oversight.
* Conclusion: Einstein Sales Assistant is a productivity tool for reps, not a solution for autonomous, large-scale lead contact. It's not the best fit.
B: Prompt Builder
* Description: Prompt Builder is a low-code tool within the Einstein 1 Platform that allows users to create reusable AI prompts for generating personalized content (e.g., emails, summaries) based on Salesforce CRM data. It integrates with generative AI models and can be embedded in workflows (e.g., via Flow) to automate content creation.
* Analysis Against Requirements:
* Scale: Alone, Prompt Builder generates content but doesn't execute outreach. When paired with automation tools like Flow or Agentforce, it can support large-scale communication by generating content for thousands of leads.
* Hyper-relevance: It uses CRM data (e.g., lead details from Data Cloud) to craft highly relevant messages or solutions tailored to each lead's context.
* Customization: It excels at producing customized communications, allowing users to define prompts that pull specific lead data for personalization.
* Efficiency: It reduces manual content creation effort, but efficiency depends on integration with an execution mechanism (e.g., Flow to send emails). Without this, it's incomplete for outreach.

NEW QUESTION # 82
Universal Containers (UC) is Implementing Service AI Grounding to enhance its customer service operations.
UC wants to ensure that its AI- generated responses are grounded in the most relevant data sources. The team needs to configure the system to include all supported objects for grounding.
Which objects should UC select to configure Service AI Grounding?

A. Case, Knowledge, and Case Notes
B. Case and Knowledge
C. Case, Case Emails, and Knowledge

Answer: B

Explanation:
Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service operations.
They aim to ensure that AI-generated responses are grounded in the most relevant data sources and need to configure the system to include all supported objects for grounding.
Supported Objects for Service AI Grounding:
* Case
* Knowledge
* Case Object:
* Role in Grounding:Provides contextual data about customer inquiries, including case details, status, and history.
* Benefit:Grounding AI responses in case data ensures that the information provided is relevant to the specific customer issue being addressed.
* Knowledge Object:
* Role in Grounding:Contains articles and documentation that offer solutions and information related to common issues.
* Benefit:Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on verified information.
* Exclusion of Other Objects:
* Case Notes and Case Emails:
* Not Supported for Grounding:While useful for internal reference, these objects are not included in the supported objects for Service AI Grounding.
* Reason:They may contain sensitive or unstructured data that is not suitable for AI grounding purposes.
Why Options A and C are Incorrect:
* Option A (Case, Knowledge, and Case Notes):
* Case Notes Not Supported:Case Notes are not among the supported objects for grounding in Service AI.
* Option C (Case, Case Emails, and Knowledge):
* Case Emails Not Supported:Case Emails are also not included in the list of supported objects for grounding.
References:
* SalesforceAgentforce SpecialistDocumentation -Service AI Grounding Configuration:Details the objects supported for grounding AI responses in Service Cloud.
* Salesforce Help -Implementing Service AI Grounding:Provides guidance on setting up grounding with Case and Knowledge objects.
* Salesforce Trailhead -Enhance Service with AI Grounding:Offers an interactive learning path on using AI grounding in service scenarios.

NEW QUESTION # 83
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response. Which information does the Resolution text provide?

A. It shows which sensitive data is masked before it is sent to the LLM.
B. It shows the response from the LLM based on the sample record.
C. It shows the full text that is sent to the Trust Layer.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs:ResolutionandResponse. These terms relate to how the prompt is processed and evaluated, particularly in the context of theEinstein Trust Layer, which ensures AI safety, compliance, and auditability. TheResolution textspecifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
* Option B: The "Response" output in the preview shows the LLM's generated text based on the sample record, not the Resolution. Resolution encompasses more than just the LLM response-it includes the entire payload sent to the Trust Layer.
* Option C: While the Trust Layer does mask sensitive data (e.g., PII) as part of its guardrails, the Resolution text doesn't specifically isolate "which sensitive data is masked." Instead, it shows the full text, including any masked portions, as processed by the Trust Layer-not a separate masking log.
* Option A: This is correct, as Resolution provides a holistic view of the text sent to the Trust Layer, aligning with its role in monitoring and auditing the AI interaction.
Thus, Option A accurately describes the purpose of the Resolution text in the prompt template preview.
References:
* Salesforce Agentforce Documentation: "Preview Prompt Templates" (Salesforce Help:https://help.
salesforce.com/s/articleView?id=sf.agentforce_prompt_preview.htm&type=5)
* Salesforce Einstein Trust Layer Documentation: "Trust Layer Outputs" (https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer.htm&type=5)

NEW QUESTION # 84
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AGENTFORCE-SPECIALIST RELIABLE EXAM SAMPLE, TEST AGENTFORCE-SPECIALIST SIMULATOR ONLINE

Agentforce-Specialist Reliable Exam Sample, Test Agentforce-Specialist Simulator Online