AGENTFORCE-SPECIALIST TRUSTWORTHY PRACTICE - DETAILED AGENTFORCE-SPECIALIST STUDY PLAN

Agentforce-Specialist Trustworthy Practice - Detailed Agentforce-Specialist Study Plan

Agentforce-Specialist Trustworthy Practice - Detailed Agentforce-Specialist Study Plan

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Tags: Agentforce-Specialist Trustworthy Practice, Detailed Agentforce-Specialist Study Plan, Agentforce-Specialist Latest Braindumps Ppt, Agentforce-Specialist Exam Questions, Agentforce-Specialist Reliable Exam Question

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Salesforce Agentforce-Specialist Exam Syllabus Topics:

TopicDetails
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
  • 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 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 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 (Q183-Q188):

NEW QUESTION # 183
The marketing team at Universal Containers is looking for a way personalize emails based on customer behavior, preferences, and purchase history.
Why should the team use Einstein Copilot as the solution?

  • A. To send automated emails to all customers
  • B. To generate relevant content when engaging with each customer
  • C. To analyze past campaign performance

Answer: B

Explanation:
Einstein Copilotis designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team atUniversal Containers, this is the perfect solution to create dynamic and relevant email content. By leveragingEinstein Copilot, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.
* Option Ais correct asEinstein Copilothelps generate real-time, personalized content based on comprehensive data about the customer.
* Option Brefers more to Einstein Analytics or
* Marketing Cloud Intelligence, andOption Cdeals with automation, which isn't the primary focus of Einstein Copilot.
References:
* Salesforce Einstein Copilot Overview:https://help.salesforce.com/s/articleView?
id=einstein_copilot_overview.htm


NEW QUESTION # 184
Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization.
What should theAgentforce Specialistrecommend?

  • A. Agentforce Analytics
  • B. Agent Studio Analytics
  • C. Agent Analytics

Answer: C

Explanation:
* Agent Analytics: This tool is specifically designed to provide usability insights for Salesforce agents. It tracks metrics like adoption rates, task completion times, and efficiency levels, helping organizations identify areas where agents excel or need additional support.
* Agentforce Analytics: This term does not correspond to a recognized Salesforce feature.
* Agent Studio Analytics: This is unrelated to analyzing agent usability, as it primarily supports customization or development features rather than providing analytics for adoption.
Thus,Agent Analyticsis the correct recommendation as it offers actionable insights to drive agent adoption and productivity.


NEW QUESTION # 185
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI-driven training content. However, users report that the AI frequently returns outdated documents. Which corrective action should UC implement to improve content relevancy?

  • A. Switch the data library source from file uploads to a Knowledge-based data library, because Salesforce Knowledge bases automatically manage document recency, ensuring current documents are returned.
  • B. Configure a custom retriever that includes a filter condition limiting retrieval to documents updated within a defined recent period, ensuring that only current content is used for AI responses.
  • C. Continue using the default retriever without filters, because periodic re-uploads will eventually phase out outdated documents without further configuration or the need for custom retrievers.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC's issue is that their file upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud's vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter (Option B). In Agentforce, a custom retriever allows UC to define specific conditions-such as a filter on a "Last Modified Date" or similar timestamp field-to limit retrieval to documents updated within a recent period (e.g., last 6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.
* Option A: Switching to a Knowledge-based Data Library (using Salesforce Knowledge articles) could work, as Knowledge articles have versioning and expiration features to manage recency.
However, this assumes UC's training content is already in Knowledge articles (not PDFs) and requires migrating all uploaded files, which is a significant shift not justified by the question's context. File- based libraries are still viable with proper filtering.
* Option B: This is the best corrective action. A custom retriever with a date filter leverages the existing file-based library, refining retrieval without changing the data source, making it practical and targeted.
* Option C: Relying on periodic re-uploads with the default retriever is passive and inefficient. It doesn't guarantee recency (old files remain indexed until manually removed) and requires ongoing manual effort, failing to proactively solve the issue.
Option B provides a precise, scalable solution to ensure content relevancy in UC's AI-driven training system.
References:
* Salesforce Agentforce Documentation: "Custom Retrievers for Data Libraries" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)
* Salesforce Data Cloud Documentation: "Filter Retrieval for AI" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)
* Trailhead: "Manage Data Libraries in Agentforce" (https://trailhead.salesforce.com/content/learn
/modules/agentforce-data-libraries)


NEW QUESTION # 186
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?

  • A. Predict most popular products from new product catalog.
  • B. Predict customer sentiment toward a promotion message.
  • C. Predict customer lifetime value of an account.

Answer: C

Explanation:
For improvingsales operations efficiency,Einstein Studiois ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predictingcustomer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions.Customer lifetime value (CLV)predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
* Option Bis the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
* Option A(customer sentiment) is typically handled through NLP models, whileOption C(product popularity) is more of a marketing analysis use case.
References:
Salesforce Einstein Studio Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_overview


NEW QUESTION # 187
Northern Trail Outfitters (NTO) wants to configure Einstein Trust Layer in its production org but is unable to see the option on the Setup page.
After provisioning Data Cloud, which step must an Al Specialist take to make this option available to NTO?

  • A. Turn on Prompt Builder.
  • B. Turn on Agent.
  • C. Turn on Einstein Generative AI.

Answer: C

Explanation:
For Northern Trail Outfitters (NTO) to configure the Einstein Trust Layer, the Einstein Generative AI feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.
* Option A (Turning on Agent) is unrelated to the setup of the Einstein Trust Layer, which focuses more on generative AI interactions and data handling.
* Option C (Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it does not enable the Einstein Trust Layer.
Salesforce Agentforce Specialist References:For more details on the Einstein Trust Layer and setup steps:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_overview.htm


NEW QUESTION # 188
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