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[General] Ace the Salesforce Agentforce-Specialist Exam preparation material with Three Fo

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【General】 Ace the Salesforce Agentforce-Specialist Exam preparation material with Three Fo

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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.

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Salesforce Certified Agentforce Specialist Sample Questions (Q26-Q31):NEW QUESTION # 26
Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization.
What should the Agentforce Specialist recommend?
  • A. Agent Studio Analytics
  • B. Agent Analytics
  • C. Agentforce Analytics
Answer: C
Explanation:
To measure adoption and usability of Agents across the organization, Agentforce Analytics is the right tool.
It provides dashboards and reports that track how Agents are being used, which actions are triggered most often, and overall performance trends. This data helps organizations drive adoption by identifying gaps, monitoring usage, and demonstrating business value.
Reference:
"Boost Adoption with Analytics Tools | Salesforce" .

NEW QUESTION # 27
What is the primary advantage of creating an individual retriever instead of the default retriever?
  • A. Individual retrievers can aggregate multiple data spaces and data model objects (DMOs) into a unified retriever output.
  • B. Individual retrievers allow the configuration of filters, specified fields, and how many results are returned.
  • C. Individual retrievers automatically generate new search indexes and dynamically update vectors.
Answer: B
Explanation:
The AgentForce Data Cloud and Retrieval Configuration Guide explains that individual retrievers offer customization flexibility beyond the default retriever. The guide states: "Individual retrievers allow specialists to define filters, select specific fields for retrieval, and configure result limits, providing fine-grained control over data recall and relevance." Option A is incorrect because aggregation across multiple data spaces or DMOs is managed through composite retrievers, not individual retrievers.
Option C is also incorrect, as retrievers do not automatically generate or update indexes - indexing is handled separately within Data Cloud.
Therefore, Option B is correct since it represents the key advantage of individual retrievers: the ability to configure filters, fields, and retrieval parameters for precision control.
References (AgentForce Documents / Study Guide):
AgentForce Data Cloud Guide: "Individual vs. Default Retriever Configuration" AgentForce Study Guide: "Fine-Tuning Retrieval Logic Using Individual Retrievers" Einstein Studio for AgentForce: "Custom Filtering and Field Selection in Retrievers"

NEW QUESTION # 28
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. Fine-Tune the standard foundational model due to the complexity of the data.
  • B. Deploy UC's own custom foundational model on this data first.
  • C. Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.
Answer: C
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.
Reference:
Salesforce Help Article: Prompt Builder for No-Code AI ("Grounding in CRM and Data Cloud" section).
Einstein GPT Implementation Guide: "Generating Summaries with Pre-Built Models."

NEW QUESTION # 29
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment.
Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
  • A. Predictive model and generative model
  • B. Predictive model
  • C. Generative model
Answer: B
Explanation:
Universal Containersshould use aPredictive modelto decide which of the three emails to send based on the customer'slifetime value scoreandmarket segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provideexplainabilityto show why a certain email was chosen, which is crucial for UC' s requirement to explain the decision-making process.
* Generative modelsare typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
* Predictive modelsoffer the ability to explain why a particular decision was made, which aligns with UC's needs.
Refer toSalesforce's Predictive AI model documentationfor more insights on how predictive models are used for segmentation and decision making.

NEW QUESTION # 30
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. Fine-Tune the standard foundational model due to the complexity of the data.
  • B. Deploy UC's own custom foundational model on this data first.
  • C. Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.
Answer: C
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.
Reference:
Salesforce Help Article: Prompt Builder for No-Code AI ("Grounding in CRM and Data Cloud" section).
Einstein GPT Implementation Guide: "Generating Summaries with Pre-Built Models."

NEW QUESTION # 31
......
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