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[General] Test Agentforce-Specialist Score Report, Agentforce-Specialist Reliable Test Que

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【General】 Test Agentforce-Specialist Score Report, Agentforce-Specialist Reliable Test Que

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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
  • 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 3
  • 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 4
  • 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 5
  • 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.

Salesforce Certified Agentforce Specialist Sample Questions (Q49-Q54):NEW QUESTION # 49
An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client.
How should theAgentforce Specialistintegrate the custom LLM into Salesforce?
  • A. Add the fine-tuned LLM in Einstein Studio Model Builder.
  • B. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
  • C. Enable model endpoint on OpenAl and make callouts to the model to generate emails.
Answer: A
Explanation:
Since security and data privacy are critical, the best option for theAgentforce Specialistis to integrate the fine- tunedLLM (Large Language Model)into Salesforce by adding it toEinstein Studio Model Builder.Einstein Studioallows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce's environment, adhering to data privacy standards.
* Option A(embedding via iFrame) is less secure and doesn't integrate deeply with Salesforce's data and security models.
* Option C(making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.
Einstein Studioprovides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found inSalesforce's Einstein Studio documentationon integrating external models.

NEW QUESTION # 50
Coral Cloud Resorts wants visibility into credit usage associated with testing.
Which feature supports this?
  • A. Testing Center
  • B. B. Digital Wallet
  • C. Agentforce Analytics
Answer: A
Explanation:
The AgentForce Testing Center Guide specifies that all testing activity-including prompt tests, structured batch runs, and evaluation sessions-consumes AI credits, which can be monitored directly through the Testing Center interface. The documentation states: "Testing Center provides visibility into testing performance, outcomes, and credit usage. Administrators can view test credit consumption metrics to understand resource utilization across agents and environments." Agentforce Analytics (Option A) provides insights into performance, engagement, and operational metrics but does not directly track credit consumption tied to testing. Digital Wallet (Option B) manages AI credit balances across orgs but does not offer test-specific tracking. Therefore, Testing Center is the correct answer since it explicitly supports monitoring credit usage during agent testing activities.
References (AgentForce Documents / Study Guide):
AgentForce Testing Center Guide: "Monitoring Credit Consumption During Testing" AgentForce Admin Handbook: "Tracking Usage and Test Costs" AgentForce Study Guide: "Testing Resource Visibility and Credit Management"

NEW QUESTION # 51
Which scenario best demonstrates when an Agentforce Data Library is most useful for improving an AI agent' s response accuracy?
  • A. When the AI agent needs to combine data from disparate sources based on mutually common data, such as Customer Id and Product Id for grounding.
  • B. When data is being retrieved from Snowflake using zero-copy for vectorization and retrieval.
  • C. When the AI agent must provide answers based on a curated set of policy documents that are stored, regularly updated, and indexed in the data library.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:The Agentforce Data Library enhances AI accuracy by grounding responses in curated, indexed data. Let's assess the scenarios.
* Option A: When the AI agent must provide answers based on a curated set of policy documents that are stored, regularly updated, and indexed in the data library.The Data Library is designed to store and index structured content (e.g., Knowledge articles, policy documents) for semantic search and grounding. It excels when an agent needs accurate, up-to-date responses from a managed corpus, like policy documents, ensuring relevance and reducing hallucinations. This is a prime use case per Salesforce documentation, making it the correct answer.
* Option B: When the AI agent needs to combine data from disparate sources based on mutually common data, such as Customer Id and Product Id for grounding.Combining disparate sources is more suited to Data Cloud's ingestion and harmonization capabilities, not the Data Library, which focuses on indexed content retrieval. This scenario is less aligned, making it incorrect.
* Option C: When data is being retrieved from Snowflake using zero-copy for vectorization and retrieval.Zero-copy integration with Snowflake is a Data Cloud feature, but the Data Library isn't specifically tied to this process-it's about indexed libraries, not direct external retrieval. This is a different context, making it incorrect.
Why Option A is Correct:The Data Library shines in curated, indexed content scenarios like policy documents, improving agent accuracy, as per Salesforce guidelines.
References:
* Salesforce Agentforce Documentation: Data Library > Use Cases- Highlights curated content grounding.
* Trailhead: Ground Your Agentforce Prompts- Describes Data Library accuracy benefits.
* Salesforce Help: Agentforce Data Library- Confirms policy document scenario.

NEW QUESTION # 52
In addition to Recipient and Sender, which object should An Agentforce utilize for inserting merge fields into a Sales email template prompt?
  • A. User Organization
  • B. Recipient Opportunities
  • C. Recipient Account
Answer: C
Explanation:
Sales Email Template Use Case:When creating a Sales email template (especially for outreach or follow-up), you often need to reference relevant details about the Account linked to the recipient.
Standard Merge Fields in Salesforce Email Templates:
Recipient (Contact, Lead, or Person receiving the email)
Sender (User sending the email)
Recipient Account (the Account related to that Contact, providing company-level details and other relevant data) Why Recipient Account?
For Sales communications, referencing the Account data (e.g., Account name, industry, or other custom fields) in an email is very common.
This is especially important for B2B scenarios where the Contact is tied to an Account.
"Recipient Opportunities" could be multiple, so it's less direct for standard email merges. The "User Organization" is more generic internal information, not typically inserted for personalization to the recipient.
References and Study Resources:
Salesforce Help & Training # Email Templates: Merge Fields
Salesforce Trailhead # "Create and Customize Email Templates in Sales Cloud" Salesforce Agentforce Specialist Study Resources (covers recommended best practices for leveraging standard objects like Account in AI-powered or prompt-based communications)

NEW QUESTION # 53
Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Agentforce Agents. How should the Agentforce Specialist monitor Agents' usability and the assignment of actions?
  • A. Run a report on the Platform Debug Logs.
  • B. Query the Agent log data using the Metadata API.
  • C. Run Agent Analytics.
Answer: C
Explanation:
Monitoring the usability and action assignments of Agentforce Agents requires insights into how agents perform, how users interact with them, and how actions are executed within conversations. Salesforce provides Agent Analytics (Option C) as a built-in capability specifically designed for this purpose. Agent Analytics offers dashboards and reports that track metrics such as agent response times, user satisfaction, action invocation frequency, and success rates. This tool allows the Agentforce Specialist to assess usability (e.g., are agents meeting user needs?) and monitor action assignments (e.g., which actions are triggered and how often), providing actionable data to optimize the pilot program.
* Option A: Platform Debug Logs are low-level logs for troubleshooting Apex, Flows, or system processes. They don't provide high-level insights into agent usability or action assignments, making this unsuitable.
* Option B: The Metadata API is used for retrieving or deploying metadata (e.g., object definitions), not runtime log data about agent performance. While Agent log data might exist, querying it via Metadata API is not a standard or documented approach for this use case.
* Option C: Agent Analytics is the dedicated solution, offering a user-friendly way to monitor conversational AI performance without requiring custom development.
Option C is the correct choice for effectively monitoring Agentforce Agents in a pilot program.
:
Salesforce Agentforce Documentation: "Agent Analytics Overview" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_analytics.htm&type=5)
Trailhead: "Agentforce for Admins" (https://trailhead.salesforce.com ... les/agentforce-for- admins)

NEW QUESTION # 54
......
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