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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Einstein Trust Layer & Security		- Data Masking 1. Configuration of privacy data entities - Trust Layer & Security Features 1. Enterprise-grade deployment
Topic 2: Prompt Engineering	30%	- Agentforce AI Setup & Configuration 1. Advanced implementation strategies - Prompt Builder & Template Creation 1. Best practices
Topic 3: Agent Force Concepts	30%	- Einstein GPT Integration 1. Advanced implementation strategies - Data Cloud & Grounding Techniques 1. Real-world applications
Topic 4: Reasoning Engine & AI Actions		- Workflow mastery 1. Custom Agent actions (Flows) 2. Standard Agent actions 3. Custom Agent actions (Apex)

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Salesforce Certified Agentforce Specialist Sample Questions (Q94-Q99):

NEW QUESTION # 94
Universal Containers (UC) is setting up a new Agentforce Service Agent. The company has sensitive medical product research stored internally and wants to ensure the agent cannot access it. What should UC da?

A. Disable the Agentforce Service Agent's ability to use any Salesforce custom object or related fields.
B. Assign the Agentforce Service Agent user the lowest possible role in the organization's hierarchy to block access.
C. Follow the principle of least privilege and avoid granting permission to view the Medical Product object or related

Answer: C

Explanation:
The AgentForce Security and Access Control Best Practices Guide emphasizes the principle of least privilege, which means granting each agent only the permissions strictly necessary to perform its defined tasks.
To prevent unauthorized access to sensitive data such as medical research, administrators should exclude permissions for the Medical Product object and related records from the AgentForce Service Agent's permission set group. This approach ensures that even if the reasoning engine processes a related query, it cannot retrieve or expose data it lacks access to.

NEW QUESTION # 95
Universal Containers wants to systematically validate agent responses before deployment using a scalable testing process. Which Testing Center approach should the company implement?

A. Manually interact with the agent in Builder until responses seem correct.
B. Use pilot users in production to flag incorrect responses post-launch.
C. Upload a structured CSV test template and run batch test cases in Testing Center.

Answer: C

Explanation:
Per the AgentForce Testing Center Operations Guide, the batch testing feature allows organizations to upload structured CSV test templates containing real-world user utterances, expected classifications, and desired outcomes. Once uploaded, these test cases are executed in bulk within the Testing Center to validate agent accuracy, reasoning consistency, and grounding before activation.
This structured approach supports repeatable, scalable testing and ensures objective evaluation across multiple topics and scenarios.

NEW QUESTION # 96
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. Ensure in the prompt that the LLM has been told to only use name value pairs in the response.
B. Ask the LLM to extract and only output the important information in the text.
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."

NEW QUESTION # 97
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 the Agentforce Specialist integrate the custom LLM into Salesforce?

A. Create an application of the custom LLM and embed it in Sales Cloud via iFrame.
B. Add the fine-tuned LLM in Einstein Studio Model Builder.
C. Enable model endpoint on OpenAl and make callouts to the model to generate emails.

Answer: B

NEW QUESTION # 98
What considerations should an Agentforce Specialist be aware of when using Record Snapshots grounding in a prompt template?

A. Email addresses associated with the object are excluded.
B. Empty data, such as fields without values or sections without limits, is filtered out.
C. Activities such as tasks and events are excluded.

Answer: C

Explanation:
Record Snapshots grounding in Agentforce prompt templates allows the AI to access and use data from a specific Salesforce record (e.g., fields and related records) to generate contextually relevant responses. However, there are specific limitations to consider. Let's analyze each option based on official documentation.

NEW QUESTION # 99
......

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