FREE PDF QUIZ 2025 SALESFORCE FANTASTIC AGENTFORCE-SPECIALIST: SALESFORCE CERTIFIED AGENTFORCE SPECIALIST TRAINING MATERIALS

Free PDF Quiz 2025 Salesforce Fantastic Agentforce-Specialist: Salesforce Certified Agentforce Specialist Training Materials

Free PDF Quiz 2025 Salesforce Fantastic Agentforce-Specialist: Salesforce Certified Agentforce Specialist Training Materials

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Salesforce Certified Agentforce Specialist Sample Questions (Q149-Q154):

NEW QUESTION # 149
What is the role of the large language model (LLM) in executing an Agent Action?

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

Answer: C

Explanation:
In Agent, 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 Agent Actions
Salesforce AI Documentation on Large Language Models


NEW QUESTION # 150
Universal Containers (UC) is implementing generative AI and wants to leverage a prompt template to provide responses to customers that gives personalized product recommendations to website visitors based on their browsing history.
Which initial step should UC take to ensure the chatbot can deliver accurate recommendations'

  • A. Collect and analyze browsing data.
  • B. Design universal product recommendations.
  • C. Write a response scrip for the chatbot.

Answer: A

Explanation:
To enable personalized product recommendations using generative AI, the foundational step for Universal Containers (UC) is collecting and analyzing browsing data (Option C). Personalized recommendations depend on understanding user behavior, which requires structured data about their browsing history. Without this data, the AI model lacks the context needed to generate relevant suggestions.
* Data Collection: UC must first aggregate browsing data (e.g., pages visited, products viewed, session duration) to build a dataset that reflects user preferences.
* Data Analysis: Analyzing this data identifies patterns (e.g., frequently viewed categories) that inform how prompts should be structured to retrieve relevant recommendations.
* Grounding in Data: Salesforce's Prompt Templates rely on grounding data to generate accurate outputs. Without analyzing browsing data, the prompt template cannot reference meaningful insights for personalization.
Options A and D are incorrect because:
* Universal recommendations (A) ignore personalization, which is the core requirement.
* Writing a response script (D) addresses chatbot interaction design, not the accuracy of recommendations.
References:
* SalesforceAgentforce SpecialistCertification Guide: Highlights the importance of grounding prompts in relevant data sources to ensure accuracy.
* Trailhead Module: "Einstein for Developers" emphasizes data preparation as a prerequisite for effective AI-driven personalization.
* Salesforce Help Documentation: Recommends analyzing user behavior data to tailor generative AI outputs in commerce use cases.


NEW QUESTION # 151
Universal Containers (UC) is experimenting with using public Generative AI models and is familiar with the language required to get the information it needs. However, it can be time-consuming for both UC's sales and service reps to type in the prompt to get the information they need, and ensure prompt consistency. Which Salesforce feature should the company use to address these concerns?

  • A. Einstein Recommendation Builder.
  • B. Einstein Prompt Builder and Prompt Templates.
  • C. Agent Builder and Action: Query Records.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC wants to streamline the use of Generative AI by reducing the time reps spend typing prompts and ensuring consistency, leveraging their existing prompt knowledge. Let's evaluate the options.
* Option A: Agent Builder and Action: Query Records.Agent Builder in Agentforce Studio creates autonomous AI agents with actions like "Query Records" to fetch data. While this could retrieve information, it's designed for agent-driven workflows, not for simplifying manual prompt entry or ensuring consistency across user inputs. This doesn't directly address UC's concerns and is incorrect.
* Option B: Einstein Prompt Builder and Prompt Templates.Einstein Prompt Builder, part of Agentforce Studio, allows users to create reusableprompt templatesthat encapsulate specific instructions and grounding for Generative AI (e.g., using public models via the Atlas Reasoning Engine). UC can predefine prompts based on their known language, saving time for reps by eliminating repetitive typing and ensuring consistency across sales and service teams. Templates can be embedded in flows, Lightning pages, or agent interactions, perfectly addressing UC's needs. This is the correct answer.
* Option C: Einstein Recommendation Builder.Einstein Recommendation Builder generates personalized recommendations (e.g., products, next best actions) using predictive AI, not Generative AI for freeform prompts. It doesn't support custom prompt creation or address time/consistency issues for reps, making it incorrect.
Why Option B is Correct:Einstein Prompt Builder's prompt templates directly tackle UC's challenges by standardizing prompts and reducing manual effort, leveraging their familiarity with Generative AI language.
This is a core feature for such use cases, as per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Einstein Prompt Builder- Details prompt templates for consistency and efficiency.
* Trailhead: Build Prompt Templates in Agentforce- Explains time-saving benefits of templates.
* Salesforce Help: Generative AI with Prompt Builder- Confirms use for streamlining rep interactions.


NEW QUESTION # 152
An Agentforce Agent has been developed with multiple topics and Agent Actions that use flows and Apex.
Which options are available for deploying these to production?

  • A. Use only change sets because the Salesforce CLI does not currently support the deployment of agent- related metadata.
  • B. Deploy the flows and Apex using normal deployment tools and manually create the agent-related items in production.
  • C. Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI
    /Metadata API.

Answer: C

Explanation:
Why is "Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI
/Metadata API" the correct answer?
When deploying an Agentforce Agent with multiple topics and Agent Actions that use flows and Apex, a complete deployment solution is required. Change sets and the Salesforce CLI/Metadata API support the deployment of flows, Apex code, and agent-related metadata.
Key Considerations for Agentforce Deployments:
* Supports Deployment of All Required Components
* Agentforce Agents include flows, Apex classes, topics, and agent actions.
* Change sets and Salesforce CLI/Metadata API allow deployment of all these components together, ensuring a smooth transition to production.
* Agentforce Metadata Can Be Deployed Using Standard Tools
* Change Sets: Allows admins to move configurations, custom objects, and metadata between Salesforce environments.
* Salesforce CLI/Metadata API: Enables scripted deployments, automating the transfer of Agentforce configurations.
* Ensures a Complete Migration Without Manual Configuration
* Deploying all components together reduces the risk of misconfiguration.
* Automating deployments using the Metadata API ensures consistency across environments.
Why Not the Other Options?
# A. Deploy the flows and Apex using normal deployment tools and manually create the agent-related items in production.
* Incorrect because manually creating agent-related items in production introduces risk and inconsistency.
* This approach is error-prone and time-consuming, especially for large Agentforce deployments.
# B. Use only change sets because the Salesforce CLI does not currently support the deployment of agent-related metadata.
* Incorrect because Salesforce CLI and Metadata API fully support Agentforce deployments.
* Change sets are useful but limited in large-scale, automated deployments.
Agentforce Specialist References
* Salesforce AI Specialist Material confirms that Agentforce metadata (flows, actions, and topics) can be deployed using Change Sets or the Metadata API.


NEW QUESTION # 153
What is true of Agentforce Testing Center?

  • A. Running tests does not consume Einstein Requests.
  • B. Running tests risks modifying CRM data in a production environment.
  • C. Agentforce Testing Center can only be used in a production environment.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:The Agentforce Testing Center is a tool in Agentforce Studio for validating agent performance. Let's evaluate the statements.
* Option A: Running tests risks modifying CRM data in a production environment.Agentforce Testing Center runs synthetic interactions in a controlled environment (e.g., sandbox or isolated test space) and doesn't modify live CRM data. It's designed for safe pre-deployment testing, making this incorrect.
* Option B: Running tests does not consume Einstein Requests.Einstein Requests are part of the usage quota for Einstein Generative AI features (e.g., prompt executions in production). Testing Center uses synthetic data to simulate interactions without invoking live AI calls that count against this quota.
Salesforce documentation confirms tests don't consume requests, making this the correct answer.
* Option C: Agentforce Testing Center can only be used in a production environment.Testing Center is available in both sandbox and production orgs, but it's primarily used pre-deployment (e.g., in sandboxes) to validate agents safely. This restriction is false, making it incorrect.
Why Option B is Correct:Not consuming Einstein Requests is a key feature of Testing Center, allowing extensive testingwithout impacting quotas, as per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Testing Center > Overview- Confirms no request consumption.
* Trailhead: Test Your Agentforce Agents- Notes quota-free testing.
* Salesforce Help: Agentforce Testing- Details safe, isolated testing.


NEW QUESTION # 154
......

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