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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
| Topic 2: Einstein Trust Layer & Security | - Trust Layer & Security Features
| |
| Topic 3: Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Topic 4: Reasoning Engine & AI Actions | - Workflow mastery
|
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NEW QUESTION # 132
An Agentforce turned on Einstein Generative AI in Setup. Now, the Agentforce Specialist would like to create custom prompt templates in Prompt Builder. However, they cannot access Prompt Builder in the Setup menu. What is causing the problem?
Answer: B
Explanation:
In order to access and create custom prompt templates in Prompt Builder, the Agentforce Specialist must have the Prompt Template Manager permission set assigned. Without this permission, they will not be able to access Prompt Builder in the Setup menu, even though Einstein Generative AI is enabled.
NEW QUESTION # 133
Universal Containers (UC) wants to use Flow to bring data from unified Data Cloud objects to prompt templates. Which type of flow should UC use?
Answer: C
Explanation:
In this scenario, Universal Containers wants to bring data from unified Data Cloud objects into prompt templates, and the best way to do that is through a Data Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.
Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.
For more detailed guidance, refer to Salesforce documentation on Data Cloud-triggered flows and Data Cloud integrations with generative AI solutions.
NEW QUESTION # 134
Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein's generative AI service.
- No data is used for LLM training or product improvements by third-
party LLMs.
- No data is retained outside of UC's Salesforce org.
- The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should the Agentforce Specialist highlight to UC that addresses these requirements?
Answer: B
Explanation:
Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data.
The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:
No data is used for training or product improvements by third-party LLMs.
No data is retained outside of the customer's Salesforce organization.
The LLM provider cannot access any customer data.
This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging generative AI capabilities.
Prompt Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.
NEW QUESTION # 135
Coral Cloud Resorts needs to ensure its booking agent executes actions in a specific sequence:
first retrieve available sessions, then verify customer eligibility, and finally create the booking. The current implementation allows the large language model (LLM) to execute these actions in any order, causing booking failures. Which approach should an AgentForce Specialist implement?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:
According to the AgentForce Orchestration and Action Sequencing Guidelines in the official documentation, deterministic execution order is best achieved by using custom state variables and conditional logic rather than relying solely on LLM reasoning or topic instructions.
AgentForce's orchestration framework allows developers to define variables that represent the successful completion of specific actions (e.g., "sessionsRetrieved," "eligibilityVerified," etc.).
Subsequent actions can then include conditional filters that only allow execution if prior steps have been completed. This approach ensures that actions execute in a strict, logical sequence - preventing the LLM from reordering steps arbitrarily.
NEW QUESTION # 136
Before activating a custom copilot action, An Agentforce would like is to understand multiple real- world user utterances to ensure the action being selected appropriately. Which tool should the Agentforce Specialist recommend?
Answer: A
Explanation:
Model Playground (specifically within the context of Generative AI and Copilot in Salesforce) allows you to test and refine the behavior of your AI models and, by extension, how your copilot actions interpret and respond to different user inputs (utterances). It's a sandbox environment where you can:
Input various user utterances.
See how the underlying Large Language Model (LLM) and the copilot's reasoning engine classify those utterances.
Observe which actions are triggered by those utterances.
Adjust the action instructions and examples to improve the copilot's understanding and ensure the correct action is chosen for specific user requests. This iterative testing is crucial for ensuring the action performs as expected in real-world scenarios.
NEW QUESTION # 137
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