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Salesforce Agentforce-Specialist Exam Syllabus Topics:

Topic	Details
Topic 1	Multi-Agent Interoperability: This domain explains Model Context Protocol (MCP), agent-to-agent communication, and when to use Agent API for system interactions.
Topic 2	AI Agents: This domain covers configuring agent behavior, understanding the reasoning engine, selecting topics and actions for agent types, managing Agent User security, choosing appropriate agent types, and connecting agents to various channels.
Topic 3	Development Lifecycle: This area addresses testing agents in Testing Center, deploying from sandbox to production, and managing agent adoption and monitoring.
Topic 4	Prompt Engineering: This section focuses on using Prompt Builder, managing user roles, creating prompt templates with field generation and flex types, selecting grounding techniques, and applying best practices for effective prompts.
Topic 5	Data Cloud for Agentforce: This domain covers Agentforce Data Library types, improving responses with unstructured data through chunking and indexing, understanding retrievers, and selecting keyword, vector, or hybrid search types.

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Salesforce Certified Agentforce Specialist (AI-201) Sample Questions (Q92-Q97):

NEW QUESTION # 92
Choose 1 option.
An Agentforce Specialist needs to create a prompt template that extracts the customer's name, phone number, and case number from a block of text, and nothing else.
How should the Agentforce Specialist structure the prompt to ensure the large language model (LLM) doesn't include extra conversation or text?

A. Ask the LLM to extract and only output the important information in the text.
B. Ensure in the prompt that the LLM has been told to only use name value pairs in the response.
C. Use well-defined output instructions and provide desired output examples.

Answer: C

Explanation:
According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well- defined output instructions and examples. The documentation specifies: "To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure." AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: "Always define your output schema clearly - for example, specify JSON or key- value pairs - and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language." Option A ("Ask the LLM to extract and only output important information") is too vague and can still produce variable or verbose responses. Option C ("Ensure the LLM has been told to only use name value pairs") is partially correct but incomplete without clear formatting and example output. Therefore, Option B is the correct choice as it aligns with AgentForce's documented standards for prompt accuracy and reliability.
References (AgentForce Documents / Study Guide):
AgentForce Prompt Engineering Best Practices Guide
AgentForce Developer Study Guide: "Defining Structured Outputs in Prompt Templates" AgentForce Technical Documentation: "Using Output Instructions and Examples for LLM Control"

NEW QUESTION # 93
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?

A. Use BYO-LLM functionality in Einstein Studio.
B. Use Apex to connect to an external LLM and ground the prompt.
C. Use Flow and External Services to bring data from an external LLM.

Answer: A

Explanation:
Bring Your Own Large Language Model (BYO-LLM)functionality inEinstein Studioallows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem.Universal Containerscan leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
* Option Bis the correct choice asEinstein Studioprovides a built-in feature to work with external models.
* Option Asuggests using Apex, butBYO-LLMfunctionality offers a more streamlined solution.
* Option Cfocuses onFlow and External Services, which is more about data integration and isn't ideal for working with LLMs.
:
Salesforce Einstein Studio BYO-LLM Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm

NEW QUESTION # 94
Universal Containers (UC) has configured a data library and wants to restrict indexing of knowledge articles to articles which are only publicly available in their knowledge base, UC also wants the agent to link sources that the large language model (LLM) grounded its response on.
Which settings should help UC with this?

A. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article. It is not possible to display articles that the LLM grounded its response in.
B. Use Data Categories to categorize publicly available articles to index. Sources are automatically displayed when knowledge articles are categorized as Public.
C. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article and select Show sources,

Answer: C

Explanation:
According to the AgentForce Data Library Configuration Guide, administrators can restrict indexing and retrieval of Knowledge articles to publicly available ones and enable source visibility for LLM-grounded responses. The documentation states:
"Within the data library settings, under Knowledge Settings, enable 'Use Public Knowledge Articles' to ensure only publicly visible content is indexed. To display citations, enable 'Show Sources' so the agent links the specific articles or data records used to ground its response." Option A correctly reflects these two documented configuration steps.
Option B is incorrect because Salesforce explicitly supports source display for transparency through the
"Show Sources" setting.
Option C incorrectly assumes that Data Categories control indexing visibility and source linking, which is handled by explicit Knowledge Settings, not categorization.
References (AgentForce Documents / Study Guide):
AgentForce Data Library Configuration Guide: "Knowledge Settings for Public Article Indexing" AgentForce Transparency and Source Attribution Notes: "Show Sources Option" AgentForce Study Guide: "Configuring Knowledge Visibility and Source Display"

NEW QUESTION # 95
Universal Containers has implemented an agent that answers questions based on Knowledge articles. Which topic and Agent Action will be shown in the Agent Builder?

A. General Q&A topic and Knowledge Article Answers action.
B. General CRM topic and Answers Questions with LLM Action.
C. General FAQ topic and Answers Questions with Knowledge Action.

Answer: C

Explanation:
UC's agent answers questions using Knowledge articles, configured in Agent Builder. Let's identify the topic and action.
* Option A: General Q&A topic and Knowledge Article Answers action."General Q&A" is not a standard topic name in Agentforce, and "Knowledge Article Answers" isn't a predefined action. This lacks specificity and doesn't match documentation, making it incorrect.
* Option B: General CRM topic and Answers Questions with LLM Action."General CRM" isn't a default topic, and "Answers Questions with LLM" suggests raw LLM responses, not Knowledge- grounded ones. This doesn't align with the Knowledge focus, making it incorrect.
* Option C: General FAQ topic and Answers Questions with Knowledge Action.In Agent Builder, the "General FAQ" topic is a common default or starting point for question-answering agents. The
"Answers Questions with Knowledge" action (sometimes styled as "Answer with Knowledge") is a prebuilt action that retrieves and grounds responses with Knowledge articles. This matches UC's implementation and is explicitly supported in documentation, making it the correct answer.
Why Option C is Correct:
"General FAQ" and "Answers Questions with Knowledge" are the standard topic-action pair for Knowledge- based question answering in Agentforce, per Salesforce resources.
References:
Salesforce Agentforce Documentation: Agent Builder > Actions - Lists "Answers Questions with Knowledge." Trailhead: Build Agents with Agentforce - Describes FAQ topics with Knowledge actions.
Salesforce Help: Knowledge in Agentforce - Confirms this configuration.

NEW QUESTION # 96
Universal Containers (UC) is rolling out an AI-powered support assistant to help customer service agents quickly retrieve relevant troubleshooting steps and policy guidelines. The assistant relies on a search index in Data Cloud that contains product manuals, policy documents, and past case resolutions. During testing, UC notices that agents are receiving too many irrelevant results from older product versions that no longer apply.
How should UC address this issue?

A. Use the default retriever, as it already searches the entire search index and provides broad coverage.
B. Modify the search index to only store documents from the last year and remove older records.
C. Create a custom retriever in Einstein Studio, and apply filters for publication date and product line.

Answer: A

Explanation:
UC's support assistant uses a Data Cloud search index for grounding, but irrelevant results from outdated product versions are an issue. Let's evaluate the options.
Option A: Modify the search index to only store documents from the last year and remove older records.
While limiting the index to recent documents could reduce irrelevant results, this requires ongoing maintenance (e.g., purging older data) and risks losing valuable historical context from past resolutions. It's a blunt approach that doesn't leverage Data Cloud's filtering capabilities, making it less optimal and incorrect.
Option B: Create a custom retriever in Einstein Studio, and apply filters for publication date and product line.
There's no "Einstein Studio" in Salesforce-possibly a typo for Agentforce Studio or Data Cloud. Custom retrievers can be created in Data Cloud, but this requires advanced configuration (e.g., custom code or Data Cloud APIs) beyond standard Agentforce setup. This is overcomplicated compared to native options, making it incorrect.
Option C: Use the default retriever, as it already searches the entire search index and provides broad coverage.
This option seems misaligned at first glance, as the default retriever's broad coverage is causing the issue.
However, the intent (based on typical Salesforce question patterns) likely implies using the default retriever with additional configuration. In Data Cloud, the default retriever searches the index, but you can apply filters (e.g., publication date, relevance) via the Data Library or prompt grounding settings to prioritize current documents. Since the question lacks an explicit filtering option, this is interpreted as the closest correct choice with refinement assumed, making it the answer by elimination and context.
Why Option C is Correct (with Caveat):
The default retriever, when paired with filters (assumed intent), allows UC to refine results without custom development. Salesforce documentation emphasizes refining retriever scope over rebuilding indexes, though the question's phrasing is suboptimal. Option C is selected as the least incorrect, assuming filter application.
References:
Salesforce Data Cloud Documentation: Search Indexes > Retrievers - Notes filter options for relevance.
Trailhead: Data Cloud for Agentforce - Covers refining search results.
Salesforce Help: Grounding with Data Cloud - Suggests default retriever with customization.

NEW QUESTION # 97
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