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100% Free Agentforce-Specialist–100% Free Latest Braindumps | Valid Test Salesforce Certified Agentforce Specialist Discount Voucher

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Salesforce Agentforce-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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.

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Salesforce Certified Agentforce Specialist Sample Questions (Q115-Q120):

NEW QUESTION # 115
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?

  • A. The number of tokens that can be processed by the LLM varies with total user demand.
  • B. The number of tokens generated by the dynamic nature of the prompt template will vary by record.
  • C. The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.

Answer: B

Explanation:
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)


NEW QUESTION # 116
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. TheAgentforce Specialistis tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production.
Which other configuration steps should theAgentforce Specialisttake in the sandbox that can be deployed to the production org?

  • A. From the Epstein setup menu, select Turn on Einstein: create custom fields to store Issue, Resolution, and Summary: create a Quick Action that updates these fields: and add the wrap up componert to the Messaging session record page layout.
  • B. create custom fields to store Issue, Resolution, and Summary; create a Quick Action that updates these fields: add the Wrap Up component to the Messaging Session record paae layout: and create Permission Set Assignments for the intended Agents.
  • C. Create custom fields to store issue, Resolution, and Summary; create a Quick Action that updates these fields: and ado the Wrap up component to the Messaging session record page lavcut.

Answer: C

Explanation:
* Context of the Question
* Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
* Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
* TheAgentforce Specialistneeds to configureWork Summaries for Enhanced Messagingin the sandbox.
* What Can Actually Be Deployed from Sandbox to Production?
* Custom Fields: Metadata that is easily created in sandbox and then deployed.
* Quick Actions: Also metadata-based and can be deployed from sandbox to production.
* Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.
* Why Option C is Correct
* No Need to Turn on Einstein in Sandbox for Deployment: Einstein Generative AI is already enabled in Production; turning it on in the sandbox is typically a manual step if you want to test, but that step itself is not "deployable" in the sense of metadata.
* Permission Set Assignments(as in Option A) are not deployable metadata. You can deploy the Permission Set itself but not the specific user assignments. Since the question specifically asks
"Which other configuration steps should be takenin the sandboxthatcanbe deployed to the production org?", user assignment is not one of them.
* Why Not Option A or B?
* Option A: Mentions creating permission set assignments for agents. This cannot be directly deployed from sandbox to Production, as permission set assignments are user-specific and considered "data," not metadata.
* Option B: Mentions "Turn on Einstein." But Einstein Generative AI is already enabled in Production. Additionally, "Turning on Einstein" is typically an org-level setting, not a deployable metadata item.
* ConclusionThe main deployable items you can reliably create and test in a sandbox, and then migrate to Production, are:
* Custom Fields(Issue, Resolution, Summary).
* A Quick Actionthat updates those fields.
* Page Layout Changeto include the Wrap Up component.
Therefore,Option Cis correct and focuses on actions that are truly deployable as metadata from a sandbox to Production.
SalesforceAgentforce SpecialistReferences & Documents
* Salesforce Trailhead:Work Summaries with Einstein GPTProvides an overview of how to configure Work Summaries, including the need for custom fields, quick actions, and UI components.
* Salesforce Documentation:Deploying Metadata Between OrgsExplains what can and cannot be deployed via change sets (e.g., custom fields, page layouts, quick actions vs. user permission set assignments).
* SalesforceAgentforce SpecialistStudy GuideOutlines which Einstein Generative AI and Work Summaries configurations are deployable as metadata.


NEW QUESTION # 117
Which business requirement presents a good use case for leveraging Einstein Prompt Builder?

  • A. Send reply to a request for proposal via a personalized email.
  • B. Forecast future sales trends based on historical data.
  • C. Identify potential high-value leads for targeted marketing campaigns.

Answer: A

Explanation:
* Context of the Question
* Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content, responses) using AI models.
* The question presents three potential use cases, asking which one best fits the capabilities of Einstein Prompt Builder.
* Einstein Prompt Builder Typical Use Cases
* Text Generation & Summaries: Great for writing or summarizing content, like responding to an email or generating text for a record field.
* Why Not Forecast Future Sales Trends or Identify Potential High-Value Leads?
* (Option A) Forecasting trends typically involves predictive analytics and modeling capabilities found in Einstein Discovery or standard reporting, not generative text solutions.
* (Option B) Identifying leads for marketing campaigns involves lead scoring or analytics, again an Einstein Discovery or Lead Scoring scenario.
* Sending a Personalized RFP Email(Option C) is a classic example of using generative AI to compose well-structured, context-aware text.
* ConclusionOption C(Send reply to a request for proposal via a personalized email) is the best match for Einstein Prompt Builder's generative text functionality.
SalesforceAgentforce SpecialistReferences & Documents
* Salesforce Documentation:Einstein Prompt Builder OverviewHighlights how to use Prompt Builder to create and customize text-based responses, especially for email or record fields.
* SalesforceAgentforce SpecialistStudy GuideExplains that generative AI features in Salesforce are designed for creating or summarizing text, not for advanced predictive use cases (like forecasting or lead scoring).


NEW QUESTION # 118
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the Agentforce Specialist recommend to achieve this use case?

  • A. Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.
  • B. Deploy UC's own custom foundational model on this data first.
  • C. Fine-Tune the standard foundational model due to the complexity of the data.

Answer: A

Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.
g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.


NEW QUESTION # 119
Universal Containers, dealing with a high volume of chat inquiries, implements Einstein Work Summaries to boost productivity.
After an agent-customer conversation, which additional information does Einstein generate and fill, apart from the "summary"'

  • A. Issue and Revolution
  • B. Sentiment Analysis and Emotion Detection
  • C. Draft Survey Request Email

Answer: A

Explanation:
Einstein Work Summaries automatically generate concise summaries of customer interactions (e.g., chat transcripts). Beyond the "summary" field, it extracts and populates Issue (key problem discussed) and Resolution (action taken to resolve the issue). These fields help agents and supervisors quickly grasp the conversation's context without reviewing the full transcript.
* Sentiment Analysis and Emotion Detection (Option A): While Einstein Conversation Insights provides sentiment scores and emotion detection, these are separate from Work Summaries.Work Summaries focus on factual summaries, not sentiment.
* Draft Survey Request Email (Option B): Not part of Work Summaries. This would require automation tools like Flow or Email Studio.
* Issue and Resolution (Option C): Directly referenced in Salesforce documentation as fields populated by Einstein Work Summaries.
References:
* Salesforce Help Article: Einstein Work Summaries
* Einstein Work Summaries focus on "key details like Issue and Resolution" alongside summaries.
* Contrast with Einstein Conversation Insights for sentiment/emotion analysis.


NEW QUESTION # 120
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