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| Section | Weight | Objectives |
|---|---|---|
| Agent Force Concepts | 30% | - Einstein GPT Integration
|
| Reasoning Engine & AI Actions | - Workflow mastery
| |
| Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Einstein Trust Layer & Security | - Trust Layer & Security Features
|
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NEW QUESTION # 70
Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management. For security purposes, UC wants the agent to verify a customer's identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed. Which approach best meets this requirement?
Answer: A
Explanation:
The AgentForce for Service Implementation Guide clearly outlines that when an agent must verify identity before performing any case-related operations, the correct method is to use a variable- based control flow. The documentation specifies: "To maintain deterministic and secure behavior, define a variable (for example, `isVerified') that stores the result of an identity verification step.
Use this variable as a conditional filter in the topic flow to ensure that case-related actions execute only when the variable equals `true'." This ensures that no sensitive or case-specific data is shared unless verification is explicitly confirmed. It provides a deterministic safeguard, as the system only proceeds with case data actions after the verification variable confirms completion.
NEW QUESTION # 71
Universal Containers (UC) needs to create a custom prompt template that can be called from a Lightning web component. Which prompt template type should UC create?
Answer: A
Explanation:
The AgentForce Developer Integration Guide specifies that Flex prompt templates are the correct type for custom or embedded integrations, such as invoking a prompt from a Lightning Web Component (LWC).
Flex templates are designed for general-purpose use cases and can be called programmatically via Apex, Flow, or LWC APIs. They offer flexible input and output structures, allowing developers to integrate AgentForce reasoning into custom applications and UI components.
NEW QUESTION # 72
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?
Answer: C
Explanation:
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market 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 provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision- making process.
Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
NEW QUESTION # 73
Universal Containers (UC) is creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements. Which prompt template type should UC use and which consideration should UC review?
Answer: C
Explanation:
Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template to populate a field with generated output, which directly aligns with the Field Generation prompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC's requirement.
Additionally, UC has enabled the Einstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas.
The consideration UC should review is whether Dynamic Fields is enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn't adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.
NEW QUESTION # 74
How should an organization use the Einstein Trust layer to audit, track, and view masked data?
Answer: B
Explanation:
The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce's AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:
Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.
NEW QUESTION # 75
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