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| Section | Weight | Objectives |
|---|---|---|
| AI Agents | 35% | - Agent User security and permissions - Agent types: Service, Sales, Employee - Topics and actions configuration - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine |
| Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding |
| Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Model Context Protocol (MCP) - Agent API usage scenarios |
| Development Lifecycle | 15% | - Deployment: sandbox to production - Monitoring, adoption and optimization - Testing agents in Testing Center - Version control and maintenance |
| Prompt Engineering | 20% | - Prompt management and user roles - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types - Grounding techniques and best practices |
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NEW QUESTION # 148
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?
Answer: A
Explanation:
When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data.
The Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI- powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions. Big Objects are more suited for archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis capabilities.
NEW QUESTION # 149
Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the "Save As" function. However, UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters. What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?
Answer: C
Explanation:
When Universal Containers creates a new Sales Email prompt template using the "Save As" function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the Agentforce Specialist should manually add the necessary hyperparameters to the new template.
Hyperparameters like Temperature, Frequency Penalty, and Presence Penalty directly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.
NEW QUESTION # 150
A service agent is looking at a custom object that stores travel information. They recently received a weather alert and now need to cancel flights for the customers that are related with this itinerary. The service agent needs to review the Knowledge articles about canceling and rebooking the customer flights. Which Agent capability helps the agent accomplish this?
Answer: A
Explanation:
In this scenario, the Agent capability that best helps the agent is its ability to execute tasks based on available actions and answer questions using data from Knowledge articles. Agent can assist the service agent by providing relevant Knowledge articles on canceling and rebooking flights, ensuring that the agent has access to the correct steps and procedures directly within the workflow.
This feature leverages the agent's existing context (the travel itinerary) and provides actionable insights or next steps from the relevant Knowledge articles to help the agent quickly resolve the customer's needs.
NEW QUESTION # 151
After an agent selects a topic, what is an important factor the reasoning engine uses to select the action?
Answer: C
Explanation:
The most crucial factor a reasoning engine uses to select an action after a topic is chosen is the priority given to each action (A). In advanced agent frameworks like AgentForce (simulated context), actions within a topic are typically not executed simply in an explicit, fixed order () unless there's no conditional logic. Instead, the reasoning engine evaluates all available actions and their associated pre-conditions (or triggers) and priorities. A priority score is often a numerical value assigned to an action that dictates its relative importance when multiple actions could potentially be executed simultaneously or when the agent must choose the 'best' action to address the current topic state. This prioritization ensures the agent handles the most critical or relevant tasks first, which is essential for efficient and goal-oriented behavior. The action's name and instructions () are descriptive for the developer but are not the primary selection criteria used by the runtime reasoning engine itself; it's the logic and priority that govern execution.
NEW QUESTION # 152
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?
Answer: C
Explanation:
The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.
Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.
NEW QUESTION # 153
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